Millimeter-wave(MMW)technology has been widely utilized in human security screening applications due to its superior penetration capabilities through clothing and safety for human exposure.However,existing methods lar...Millimeter-wave(MMW)technology has been widely utilized in human security screening applications due to its superior penetration capabilities through clothing and safety for human exposure.However,existing methods largely rely on fixed polarization modes,neglecting the potential insights from variations in target echoes with respect to incident polarization.This study provides a theoretical analysis of the cross-polarization echo power as a function of the incident polarization angle under linear polarization conditions.Additionally,based on the transmission characteristics of multi-layer medium,we extend the depth spectrum model employed in direct detection to accommodate scenarios involving multi-layered structures.Building on this foundation,by obtaining multiple depth spectra through polarization angle scanning,we propose the Polarization Angle-Depth Matrix to characterize targets across both the polarization angle and depth dimensions in direct detection.Simulations and experimental validations confirm its accuracy and practical value in detecting concealed weapons in human security screening scenarios.展开更多
In this study,a multifunctional aptamer-conjugated magnetic covalent organic framework(COF)-CuO/Au nanozyme(MCOF-CuO/Au@apt)was developed as a“three-in-one”platform for dual-signal colorimetric and fluorescent detec...In this study,a multifunctional aptamer-conjugated magnetic covalent organic framework(COF)-CuO/Au nanozyme(MCOF-CuO/Au@apt)was developed as a“three-in-one”platform for dual-signal colorimetric and fluorescent detection of Vibrio parahaemolyticus.The nanozyme integrated magnetic separation,peroxidase-like catalytic activity,and specific target recognition through an aptamer-based strategy.Upon binding to V.parahaemolyticus,the catalytic oxidation of tetra-aminophenylethylene(TPE-4A)by the nanozyme was selectively inhibited,resulting in distinct colorimetric and fluorescent signals that significantly enhanced the detection accuracy and reliability.The proposed method exhibited high sensitivity,with limits of detection(LOD)of 21 and 7 CFU/mL for the colorimetric and fluorescent assays,respectively.The performance of this method was validated using real seafood samples,including Penaeus vannamei,Mytilus coruscus,and Crassostrea gigas,which showed high recovery rates(101.11%-107.30%)and excellent reproducibility.The system also demonstrated strong specificity and accuracy under various conditions,confirming its robustness and practical applicability.Collectively,this innovative platform presents a promising solution for the rapid,versatile,and sensitive detection of V.parahaemolyticus in seafood,with considerable potential to advance food safety diagnosis and on-site monitoring.展开更多
【Objective】This study aimed to establish a quintuple PCR method for rapid and simultaneous detection of Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provided tech...【Objective】This study aimed to establish a quintuple PCR method for rapid and simultaneous detection of Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provided technical support for early diagnosis of various soil-borne diseases on ginger.【Method】For five types of soil-borne pathogens causing ginger bacterial wilt and rhizome rot,specific primer combinations were designed and screened,the optimal quintuple reaction system was established by exploring optimal primer concentrations,annealing temperature,and sensitivity,and was applied to detect field plant samples to verify its utility.【Result】Specific primers pairs Rs1F/Rs1R,En1F/En1R,and Py1F/Py1R were designed according to flic gene of Ralstonia solanacearum,rpoB gene of Enterobacter spp.,and 18S rDNA of Pythium spp.,and combined with reported Fusarium spp.specific primers Fu3/Fu4 and specific primers 23SPecF/23SPecR of Pectobacterium spp.,a quintuple PCR reaction system for ginger soil-borne pathogens has been established(25.00μL):above primer dosage was 1.20,0.20,0.60,1.60,and 0.15μL respectively;2×PCR Mix 12.50μL;DNA templates of different pathogens were 1.00μL each;added ddH_(2)O to 25.00μL.Annealing temperature was optimized to 55.4℃.The specific fragments with sizes of 516,370,266,207,and 159 bp could be amplified simultaneously in the established quintuple PCR system,and the detection limit of this system for Ralstonia solanacearum,Enterobacter spp.and Pythium spp.reached 10^(-1)pg/μL,for Fusarium spp.and Pectobacterium spp.was 1 pg/μL,and for detecting five pathogens simultaneously was 10^(3)pg/μL.The multiplex PCR system established in this study could successfully detect the diseased plant samples from the field.【Conclusion】The quintuple PCR system established is able to rapid ly and accurately detect Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provides a useful tool for timely diagnosis and epidemic monitoring of various soil-borne diseases of ginger.展开更多
Multi-modality sensor fusion has emerged as a prevailing trend in 3D object detection tasks.However,existing research predominantly emphasizes the efficient fusion of data from diverse sensors,overlooking the potentia...Multi-modality sensor fusion has emerged as a prevailing trend in 3D object detection tasks.However,existing research predominantly emphasizes the efficient fusion of data from diverse sensors,overlooking the potential severe consequences of calibration failures.In this paper,we present an innovative analysis and prediction of scenarios that could lead to fusion algorithm failures,along with introducing remedial measures to enhance model robustness.Specifically,leveraging our predicted outcomes,we proactively generate similar hazardous scenarios during the model training phase to facilitate generalization capabilities.Subsequently,we introduce a query mechanism during data fusion to identify the appropriate fusion target in the event of miscalibration.Evaluation on the nuScenes dataset demonstrates that our approach can mitigate model instability by up to 90%,and our framework can be seamlessly adapted to other fusion algorithms.展开更多
Re-entry gliding vehicles exhibit high maneuverability,making trajectory prediction a key factor in the effectiveness of defense systems.To overcome the limited fitting accuracy of existing methods and their poor adap...Re-entry gliding vehicles exhibit high maneuverability,making trajectory prediction a key factor in the effectiveness of defense systems.To overcome the limited fitting accuracy of existing methods and their poor adaptability to maneuver mode mutations,a trajectory prediction method is proposed that integrates online maneuver mode identification with dynamic modeling.Characteristic parameters are extracted from tracking data for parameterized modeling,enabling real-time identification of maneuver modes.In addition,a maneuver detection mechanism based on higher-order cumulants is introduced to detect lateral maneuver mutations and optimize the use of historical data.Simulation results show that the proposed method achieves accurate trajectory prediction during the glide phase and maintains high accuracy under maneuver mutations,significantly enhancing the prediction performance of both three-dimensional trajectories and ground tracks.展开更多
A measurement system for the scattering characteristics of warhead fragments based on high-speed imaging systems offers advantages such as simple deployment,flexible maneuverability,and high spatiotemporal resolution,...A measurement system for the scattering characteristics of warhead fragments based on high-speed imaging systems offers advantages such as simple deployment,flexible maneuverability,and high spatiotemporal resolution,enabling the acquisition of full-process data of the fragment scattering process.However,mismatches between camera frame rates and target velocities can lead to long motion blur tails of high-speed fragment targets,resulting in low signal-to-noise ratios and rendering conventional detection algorithms ineffective in dynamic strong interference testing environments.In this study,we propose a detection framework centered on dynamic strong interference disturbance signal separation and suppression.We introduce a mixture Gaussian model constrained under a joint spatialtemporal-transform domain Dirichlet process,combined with total variation regularization to achieve disturbance signal suppression.Experimental results demonstrate that the proposed disturbance suppression method can be integrated with certain conventional motion target detection tasks,enabling adaptation to real-world data to a certain extent.Moreover,we provide a specific implementation of this process,which achieves a detection rate close to 100%with an approximate 0%false alarm rate in multiple sets of real target field test data.This research effectively advances the development of the field of damage parameter testing.展开更多
With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a c...With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a convenient and visual technique with low equipment requirements and high sensitivity for the field detection of GM plants is still lacking.On the basis of the existing recombinase polymerase amplification(RPA)technique,we developed a multiplex RPA(multi-RPA)method that can simultaneously detect three transgenic elements,including the cauliflower mosaic virus 35S gene(CaMV35S)promoter,neomycin phosphotransferaseⅡgene(NptⅡ)and hygromycin B phosphotransferase gene(Hyg),thus improving the detection rate.Moreover,we coupled this multi-RPA technique with the CRISPR/Cas12a reporter system,which enabled the detection results to be clearly observed by naked eyes under ultraviolet(UV)light(254 nm;which could be achieved by a portable UV flashlight),therefore establishing a multi-RPA visual detection technique.Compared with the traditional test strip detection method,this multi-RPA-CRISPR/Cas12a technique has the higher specificity,higher sensitivity,wider application range and lower cost.Compared with other polymerase chain reaction(PCR)techniques,it also has the advantages of low equipment requirements and visualization,making it a potentially feasible method for the field detection of GM plants.展开更多
Cucumber is one of the most important vegetables and economic crops in the world.The occurrence of fungal diseases in cucumbers seriously threatens the safety of cucumber production,with powdery mildew being one of th...Cucumber is one of the most important vegetables and economic crops in the world.The occurrence of fungal diseases in cucumbers seriously threatens the safety of cucumber production,with powdery mildew being one of the most common fungal diseases.With the rapid development of computer technology,more and more deep learning algorithms are being applied to identify powdery mildew fungus.However,existing algorithms suffer from low accuracy in recognizing small and occluded targets,as well as insufficient localization precision.To address this issue,the parallelized patch-aware attention(PPA)module was firstly introduced into the backbone network of YOLO v8s.By employing a parallel multi-branch structure and attention mechanism,it effectively captured multi-scale features of small targets,preserved critical information during multiple downsampling processes,and enhanced the performance of small target detection.Additionally,the global-to-local spatial aggregation(GLSA)module was introduced into the neck,which combined global contextual information with local detail features,significantly improving the model’s feature representation capability.This module enhanced the detection performance for small targets and complex scenes by better capturing multi-scale features.Experimental results showed that PG-YOLO v8s significantly improved powdery mildew fungus detection performance compared with YOLO v8s.The network achieved high precision in detecting powdery mildew fungus,with notable improvements in the detection accuracy of small and occluded targets.The research result can provide a high-throughput method for detecting powdery mildew fungus,enabling precise early detection and guiding early intelligent decision-making in cucumber production.This approach can help to improve disease control efficiency,ensure cucumber yield and quality,and it was of great significance for the sustainable development of agricultural production.展开更多
[Objective]The accurate identification of maize tassels is critical for the production of hybrid seed.Existing object detection models in complex farmland scenarios face limitations such as restricted data diversity,i...[Objective]The accurate identification of maize tassels is critical for the production of hybrid seed.Existing object detection models in complex farmland scenarios face limitations such as restricted data diversity,insufficient feature extraction,high computational load,and low detection efficiency.To address these challenges,a real-time field maize tassel detection model,LightTassel-YOLO(You Only Look Once)based on an improved YOLOv11n is proposed.The model is designed to quickly and accurately identify maize tassels,enabling efficient operation of detasseling unmanned aerial vehicles(UAVs)and reducing the impact of manual intervention.[Methods]Data was continuously collected during the tasseling stage of maize from 2023 to 2024 using UAVs,establishing a large-scale,high-quality maize tassel dataset that covered different maize tasseling stages,multiple varieties,varying altitudes,and diverse meteorological conditions.First,EfficientViT(Efficient vision transformer)was applied as the backbone network to enhance the ability to perceive information across multi-scale features.Second,the C2PSA-CPCA(Convolutional block with parallel spatial attention with channel prior convolutional attention)module was designed to dynamically assign attention weights to the channel and spatial dimensions of feature maps,effectively enhancing the network's capability to extract target features while reducing computational complexity.Finally,the C3k2-SCConv module was constructed to facilitate representative feature learning and achieve low-cost spatial feature reconstruction,thereby improving the model's detection accuracy.[Results and Discussions]The results demonstrated that LightTassel-YOLO provided a reliable method for maize tassel detection.The final model achieved an accuracy of 92.6%,a recall of 89.1%,and an AP@0.5 of 94.7%,representing improvements of 2.5,3.8 and 4.0 percentage points over the baseline model YOLOv11n,respectively.The model had only 3.23 M parameters and a computational cost of 6.7 GFLOPs.In addition,LightTassel-YOLO was compared with mainstream object detection algorithms such as Faster R-CNN,SSD,and multiple versions of the YOLO series.The results demonstrated that the proposed method outperformed these algorithms in overall performance and exhibits excellent adaptability in typical field scenarios.[Conclusions]The proposed method provides an effective theoretical framework for precise maize tassel monitoring and holds significant potential for advancing intelligent field management practices.展开更多
To address the challenges of low detection accuracy caused by the diverse species,significant size variations,and complex growth environments of wheat pests in natural settings,a PSA-YOLO11n algorithm is proposed to e...To address the challenges of low detection accuracy caused by the diverse species,significant size variations,and complex growth environments of wheat pests in natural settings,a PSA-YOLO11n algorithm is proposed to enhance detection precision.Building upon the YOLO11n framework,the proposed improvements include three key components:1)SimCSPSPPF in Backbone:An improved Spatial Pyramid Pooling-Fast(SPPF)module,SimCSPSPPF,is integrated into the Backbone to reduce the number of channels in the hidden layers,thereby accelerating model training.2)PEC in Neck:The standard convolution layers in the Neck are replaced with Perception Enhancement Convolutions(PEC)to improve multi-scale feature extraction capabilities,enhancing detection speed.3)AWIoU Loss Function:The regression loss function is replaced with Adequate Wise IoU(AWIoU),addressing issues of bounding box distortion caused by the diversity in pest species and size variations,thereby improving the precision of bounding box localization.Experimental evaluations on the IP102 dataset demonstrate that PSA-YOLO11n achieves a mean Average Precision(mAP)of 89.10%,surpassing YOLO11n by 0.8%.Comparisons with other mainstream algorithms,including Faster R-CNN,RetinaNet,YOLOv5s,YOLOv8n,YOLOv10n,and YOLO11n,confirm that PSA-YOLO11n outperforms all baselines in terms of detection performance.These results highlight the algorithm’s capability to significantly improve the detection accuracy of multi-scale wheat pests in natural environments,providing an effective solution for pest management in wheat production.展开更多
Human disturbance activities is one of the main reasons for inducing geohazards.Ecological impact assessment metrics of roads are inconsistent criteria and multiple.From the perspective of visual observation,the envir...Human disturbance activities is one of the main reasons for inducing geohazards.Ecological impact assessment metrics of roads are inconsistent criteria and multiple.From the perspective of visual observation,the environment damage can be shown through detecting the uncovered area of vegetation in the images along road.To realize this,an end-to-end environment damage detection model based on convolutional neural network is proposed.A 50-layer residual network is used to extract feature map.The initial parameters are optimized by transfer learning.An example is shown by this method.The dataset including cliff and landslide damage are collected by us along road in Shennongjia national forest park.Results show 0.4703 average precision(AP)rating for cliff damage and 0.4809 average precision(AP)rating for landslide damage.Compared with YOLOv3,our model shows a better accuracy in cliff and landslide detection although a certain amount of speed is sacrificed.展开更多
The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced met...The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.展开更多
For the characteristics of small,dense distribution,high diversity of defects and high precision and fast detection in the process of PCB(Printed Circuit Board)defect detection,a defect detection algorithm based on YO...For the characteristics of small,dense distribution,high diversity of defects and high precision and fast detection in the process of PCB(Printed Circuit Board)defect detection,a defect detection algorithm based on YOLOv8n was proposed in this study.Firstly,the original C2f module of YOLOv8n was improved into a C2FFaster-EMA module to reduce the number of parameters and floating-point operations(FLOPs).Additionally,the WIoUv3 loss function was introduced to mitigate the negative impact of low-quality defect images on model training.Consequently,a reduction in model size and an enhancement in detection precision were achieved.Finally,the ablation and comparative experiments were conducted on an augmented Deep PCB dataset,and the generalization experiments were performed on the PCB Defect-Augmented dataset.The results indicated that the proposed model reduces the number of parameters by 23.3%and FLOPs by 20%,P by 0.7%,mAP@0.5 by 0.3%,and mAP@0.5:0.95 by 3.9%,respectively,compared to the original YOLOv8n model.Furthermore,the comparative experiments demonstrated that the proposed model achieves higher accuracy and mAP compared to YOLOv5n and YOLOv5s.It was concluded that the proposed method satisfies the requirements for both accuracy and speed in PCB defect detection.展开更多
Detection of target analytes at low concentrations is significant in various fields,including pharmaceuticals,healthcare,and environmental protection.Theophylline(TP),a natural alkaloid used as a bronchodilator to tre...Detection of target analytes at low concentrations is significant in various fields,including pharmaceuticals,healthcare,and environmental protection.Theophylline(TP),a natural alkaloid used as a bronchodilator to treat respiratory disorders such as asthma,bronchitis,and emphysema,has a narrow therapeutic window with a safe plasma concentration ranging from 55.5-111.0μmol·L^(-1)in adults.Accurate monitoring of TP levels is essential because too low or too high can cause se-rious side effects.In this regard,non-enzymatic electrochemical sensors offer a practical solution with rapidity,portability,and high sensitivity.This article aims to provide a comprehensive review of the recent developments of non-enzymatic electrochemical sensors for TP detection,highlighting the basic principles,electro-oxidation mechanisms,catalytic effects,and the role of modifying materials on electrode performance.Carbon-based electrodes such as glassy carbon electrodes(GCEs),carbon paste electrodes(CPEs),and carbon screen-printed electrodes(SPCEs)have become the primary choices for non-enzymatic sensors due to their chemical stability,low cost,and flexibility in modification.This article identifies the sig-nificant contribution of various modifying materials,including nanomaterials such as carbon nanotubes(CNTs),graphene,metal oxides,and multi-element nanocomposites.These modifications enhance sensors’electron transfer,sensitivity,and selectivity in detecting TP at low concentrations in complex media such as blood plasma and pharmaceutical samples.The electro-oxidation mechanism of TP is also discussed in depth,emphasizing the hydroxyl and carbonyl reaction pathways strongly influenced by pH and electrode materials.These mechanisms guide the selection of the appropriate electrode ma-terial for a particular application.The main contribution of this article is to identify superior modifying materials that can improve the performance of non-enzymatic electrochemical sensors.In a recent study,the combination of multi-element nanocomposites based on titanium dioxide(TiO_(2)),CNTs,and gold nanoparticles(AuNPs)resulted in the lowest detection limit of 3×10^(-5)μmol·L^(-1),reflecting the great potential of these materials for developing high-performance electrochemical sensors.The main conclusion of this article is the importance of a multidisciplinary approach in electrode material design to support the sensitivity and selectivity of TP detection.In addition,there is still a research gap in understanding TP’s more detailed oxidation mechanism,especially under pH variations and complex environments.Therefore,further research on electrode modification and analysis of the TP oxidation mechanism are urgently needed to improve the accuracy and sta-bility of the sensor while expanding its applications in pharmaceutical monitoring and medical diagnostics.By integrating various innovative materials and technical approaches,this review is expected to be an essential reference for developing efficient and affordable non-enzymatic electrochemical sensors.展开更多
The abnormal metabolic activity of the tumor can increase the oxygen consumption in tumor cells,and the poor blood perfusion often happens in tumor regions as well,which are the main reasons that result in a hypoxic s...The abnormal metabolic activity of the tumor can increase the oxygen consumption in tumor cells,and the poor blood perfusion often happens in tumor regions as well,which are the main reasons that result in a hypoxic situation in the tumor.A fluorescence probe,AQD,with selective response toward hypoxia was designed for the detection of hypoxic tumor cells,which was obtained by the covalent connection of a large planar conjugated fluorophore with good fluorescence stability and a N,N-dimethylaniline moiety via the azo bond.The introduction of the azo bond in AQD caused significant fluorescence emission quenching,and the probe was reduced under hypoxic conditions to release the fluorophore via breaking the azo bond,resulting in the gradual recovery of fluorescence emission.Probe AQD exhibited a remarkable fluorescence response in hypoxic conditions,high selectivity,and good biocompatibility,which was successfully used for the imaging of hypoxic tumor cells and realized the detection of hypoxic A549 cells.展开更多
Bovine serum albumin(BSA)and glycine(Gly)dual-ligand-modified copper nanoclusters(BSA-Gly CuNCs)with high fluorescence intensity were synthesized by a one-pot strategy.Based on the competitive fluorescence quenching a...Bovine serum albumin(BSA)and glycine(Gly)dual-ligand-modified copper nanoclusters(BSA-Gly CuNCs)with high fluorescence intensity were synthesized by a one-pot strategy.Based on the competitive fluorescence quenching and dynamic quenching effects of ornidazole(ONZ)on BSA-Gly CuNCs,a simple and sensitive detection method for ONZ was successfully developed.The experimental results demonstrate that the addition of the small molecule Gly can more effectively protect CuNCs,and thus enhance its fluorescence intensity and stability.The proposed assay allowed for the detection of ONZ in a linear range of 0.28 to 52.60μmol·L^(-1)and a detection limit of 0.069μmol·L^(-1).Compared with the single-ligand-modified CuNCs,dual-ligand-modified BSA-Gly CuNCs had higher fluorescence intensity,stability,and sensing ability and were successfully applied to evaluate ONZ in actual ONZ tablets.展开更多
This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,an...This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,and a CMOS sensor.In view of the significant contrast between face and background in thermal infra⁃red images,this paper explores a suitable accuracy-latency tradeoff for thermal face detection and proposes a tiny,lightweight detector named YOLO-Fastest-IR.Four YOLO-Fastest-IR models(IR0 to IR3)with different scales are designed based on YOLO-Fastest.To train and evaluate these lightweight models,a multi-user low-resolution thermal face database(RGBT-MLTF)was collected,and the four networks were trained.Experiments demon⁃strate that the lightweight convolutional neural network performs well in thermal infrared face detection tasks.The proposed algorithm outperforms existing face detection methods in both positioning accuracy and speed,making it more suitable for deployment on mobile platforms or embedded devices.After obtaining the region of interest(ROI)in the infrared(IR)image,the RGB camera is guided by the thermal infrared face detection results to achieve fine positioning of the RGB face.Experimental results show that YOLO-Fastest-IR achieves a frame rate of 92.9 FPS on a Raspberry Pi 4B and successfully detects 97.4%of faces in the RGBT-MLTF test set.Ultimate⁃ly,an infrared temperature measurement system with low cost,strong robustness,and high real-time perfor⁃mance was integrated,achieving a temperature measurement accuracy of 0.3℃.展开更多
Lunar impact crater detection is crucial for lunar surface studies and spacecraft landing missions,yet deep learning still struggles with accurately detecting small craters,especially when relying on incomplete catalo...Lunar impact crater detection is crucial for lunar surface studies and spacecraft landing missions,yet deep learning still struggles with accurately detecting small craters,especially when relying on incomplete catalogs.In this work,we integrate Digital Elevation Model(DEM)data to construct a high-quality dataset enriched with slope information,enabling a detailed analysis of crater features and effectively improving detection performance in complex terrains and low-contrast areas.Based on this foundation,we propose a novel two-stage detection network,MSFNet,which leverages multi-scale adaptive feature fusion and multisize ROI pooling to enhance the recognition of craters across various scales.Experimental results demonstrate that MSFNet achieves an F1 score of 74.8%on Test Region1 and a recall rate of 87%for craters with diameters larger than 2 km.Moreover,it shows exceptional performance in detecting sub-kilometer craters by successfully identifying a large number of high-confidence,previously unlabeled targets with a low false detection rate confirmed through manual review.This approach offers an efficient and reliable deep learning solution for lunar impact crater detection.展开更多
Two new complexes,[Zn_(2)(L1)(HL1)(NO_(3))]·CH_(3)OH(1)and[Zn_(3)(L2)(L3)_(3)Cl]·CH_(3)OH(2),were successfully synthesized by‘one-pot’method based on cinnoline-3-ylhydrazine ligand and zinc with 2-hydroxy-...Two new complexes,[Zn_(2)(L1)(HL1)(NO_(3))]·CH_(3)OH(1)and[Zn_(3)(L2)(L3)_(3)Cl]·CH_(3)OH(2),were successfully synthesized by‘one-pot’method based on cinnoline-3-ylhydrazine ligand and zinc with 2-hydroxy-4-methoxybenzaldehyde and 2-hydroxy-3-methoxybenzaldehyde ligands,respectively,where H_(2)L1=5-methoxy-2-(phthalazin-1-ylhydrazonomethyl)-phenol,H_(2)L2=2-methoxy-6-(phthalazin-1-yl-hydrazonomethyl)-phenol,HL3=2-(1,8-dihydro-[1,2,4]triazolo[3,4-α]phthalazin-3-yl)-6-methoxy-phenol.Complexes 1 and 2 were characterized by infrared spectroscopy,elemental analysis,single-crystal X-ray diffraction,powder X-ray diffraction,etc.It is worth noting that the cinnolin-3-yl-hydrazine ligand and 2-hydroxy-3-methoxybenzaldehyde form two types of Schiff bases(H_(2)L2 and HL3)when in situ reacting and coordinating with Zn(Ⅱ),and HL3 also has two coordination modes.In addition,the fluorescence performance showed that complex 1 can achieve selective and sensitive sensing of Al^(3+)in water with a detection limit of 6.37μmol·L^(-1).CCDC:2413978,1;2413979,2.展开更多
In recent years,with the development of synthetic aperture radar(SAR)technology and the widespread application of deep learning,lightweight detection of SAR images has emerged as a research direction.The ultimate goal...In recent years,with the development of synthetic aperture radar(SAR)technology and the widespread application of deep learning,lightweight detection of SAR images has emerged as a research direction.The ultimate goal is to reduce computational and storage requirements while ensuring detection accuracy and reliability,making it an ideal choice for achieving rapid response and efficient processing.In this regard,a lightweight SAR ship target detection algorithm based on YOLOv8 was proposed in this study.Firstly,the C2f-Sc module was designed by fusing the C2f in the backbone network with the ScConv to reduce spatial redundancy and channel redundancy between features in convolutional neural networks.At the same time,the Ghost module was introduced into the neck network to effectively reduce model parameters and computational complexity.A relatively lightweight EMA attention mechanism was added to the neck network to promote the effective fusion of features at different levels.Experimental results showed that the Parameters and GFLOPs of the improved model are reduced by 8.5%and 7.0%when mAP@0.5 and mAP@0.5:0.95 are increased by 0.7%and 1.8%,respectively.It makes the model lightweight and improves the detection accuracy,which has certain application value.展开更多
基金Supported by the National Key Research and Development Program of China(2023YFF0715000)。
文摘Millimeter-wave(MMW)technology has been widely utilized in human security screening applications due to its superior penetration capabilities through clothing and safety for human exposure.However,existing methods largely rely on fixed polarization modes,neglecting the potential insights from variations in target echoes with respect to incident polarization.This study provides a theoretical analysis of the cross-polarization echo power as a function of the incident polarization angle under linear polarization conditions.Additionally,based on the transmission characteristics of multi-layer medium,we extend the depth spectrum model employed in direct detection to accommodate scenarios involving multi-layered structures.Building on this foundation,by obtaining multiple depth spectra through polarization angle scanning,we propose the Polarization Angle-Depth Matrix to characterize targets across both the polarization angle and depth dimensions in direct detection.Simulations and experimental validations confirm its accuracy and practical value in detecting concealed weapons in human security screening scenarios.
文摘In this study,a multifunctional aptamer-conjugated magnetic covalent organic framework(COF)-CuO/Au nanozyme(MCOF-CuO/Au@apt)was developed as a“three-in-one”platform for dual-signal colorimetric and fluorescent detection of Vibrio parahaemolyticus.The nanozyme integrated magnetic separation,peroxidase-like catalytic activity,and specific target recognition through an aptamer-based strategy.Upon binding to V.parahaemolyticus,the catalytic oxidation of tetra-aminophenylethylene(TPE-4A)by the nanozyme was selectively inhibited,resulting in distinct colorimetric and fluorescent signals that significantly enhanced the detection accuracy and reliability.The proposed method exhibited high sensitivity,with limits of detection(LOD)of 21 and 7 CFU/mL for the colorimetric and fluorescent assays,respectively.The performance of this method was validated using real seafood samples,including Penaeus vannamei,Mytilus coruscus,and Crassostrea gigas,which showed high recovery rates(101.11%-107.30%)and excellent reproducibility.The system also demonstrated strong specificity and accuracy under various conditions,confirming its robustness and practical applicability.Collectively,this innovative platform presents a promising solution for the rapid,versatile,and sensitive detection of V.parahaemolyticus in seafood,with considerable potential to advance food safety diagnosis and on-site monitoring.
基金National Natural Science Foundation of China(32270237)Guangxi Key Research and Development Plan Project(Guike AB21238002)Basic Scientific Research Project of Guangxi Academy of Agricultural Sciences(Guinongke 2024YP082)。
文摘【Objective】This study aimed to establish a quintuple PCR method for rapid and simultaneous detection of Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provided technical support for early diagnosis of various soil-borne diseases on ginger.【Method】For five types of soil-borne pathogens causing ginger bacterial wilt and rhizome rot,specific primer combinations were designed and screened,the optimal quintuple reaction system was established by exploring optimal primer concentrations,annealing temperature,and sensitivity,and was applied to detect field plant samples to verify its utility.【Result】Specific primers pairs Rs1F/Rs1R,En1F/En1R,and Py1F/Py1R were designed according to flic gene of Ralstonia solanacearum,rpoB gene of Enterobacter spp.,and 18S rDNA of Pythium spp.,and combined with reported Fusarium spp.specific primers Fu3/Fu4 and specific primers 23SPecF/23SPecR of Pectobacterium spp.,a quintuple PCR reaction system for ginger soil-borne pathogens has been established(25.00μL):above primer dosage was 1.20,0.20,0.60,1.60,and 0.15μL respectively;2×PCR Mix 12.50μL;DNA templates of different pathogens were 1.00μL each;added ddH_(2)O to 25.00μL.Annealing temperature was optimized to 55.4℃.The specific fragments with sizes of 516,370,266,207,and 159 bp could be amplified simultaneously in the established quintuple PCR system,and the detection limit of this system for Ralstonia solanacearum,Enterobacter spp.and Pythium spp.reached 10^(-1)pg/μL,for Fusarium spp.and Pectobacterium spp.was 1 pg/μL,and for detecting five pathogens simultaneously was 10^(3)pg/μL.The multiplex PCR system established in this study could successfully detect the diseased plant samples from the field.【Conclusion】The quintuple PCR system established is able to rapid ly and accurately detect Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provides a useful tool for timely diagnosis and epidemic monitoring of various soil-borne diseases of ginger.
文摘Multi-modality sensor fusion has emerged as a prevailing trend in 3D object detection tasks.However,existing research predominantly emphasizes the efficient fusion of data from diverse sensors,overlooking the potential severe consequences of calibration failures.In this paper,we present an innovative analysis and prediction of scenarios that could lead to fusion algorithm failures,along with introducing remedial measures to enhance model robustness.Specifically,leveraging our predicted outcomes,we proactively generate similar hazardous scenarios during the model training phase to facilitate generalization capabilities.Subsequently,we introduce a query mechanism during data fusion to identify the appropriate fusion target in the event of miscalibration.Evaluation on the nuScenes dataset demonstrates that our approach can mitigate model instability by up to 90%,and our framework can be seamlessly adapted to other fusion algorithms.
基金supported by the National Natural Science Foundation of China(12302056)the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(GZC20233445)。
文摘Re-entry gliding vehicles exhibit high maneuverability,making trajectory prediction a key factor in the effectiveness of defense systems.To overcome the limited fitting accuracy of existing methods and their poor adaptability to maneuver mode mutations,a trajectory prediction method is proposed that integrates online maneuver mode identification with dynamic modeling.Characteristic parameters are extracted from tracking data for parameterized modeling,enabling real-time identification of maneuver modes.In addition,a maneuver detection mechanism based on higher-order cumulants is introduced to detect lateral maneuver mutations and optimize the use of historical data.Simulation results show that the proposed method achieves accurate trajectory prediction during the glide phase and maintains high accuracy under maneuver mutations,significantly enhancing the prediction performance of both three-dimensional trajectories and ground tracks.
文摘A measurement system for the scattering characteristics of warhead fragments based on high-speed imaging systems offers advantages such as simple deployment,flexible maneuverability,and high spatiotemporal resolution,enabling the acquisition of full-process data of the fragment scattering process.However,mismatches between camera frame rates and target velocities can lead to long motion blur tails of high-speed fragment targets,resulting in low signal-to-noise ratios and rendering conventional detection algorithms ineffective in dynamic strong interference testing environments.In this study,we propose a detection framework centered on dynamic strong interference disturbance signal separation and suppression.We introduce a mixture Gaussian model constrained under a joint spatialtemporal-transform domain Dirichlet process,combined with total variation regularization to achieve disturbance signal suppression.Experimental results demonstrate that the proposed disturbance suppression method can be integrated with certain conventional motion target detection tasks,enabling adaptation to real-world data to a certain extent.Moreover,we provide a specific implementation of this process,which achieves a detection rate close to 100%with an approximate 0%false alarm rate in multiple sets of real target field test data.This research effectively advances the development of the field of damage parameter testing.
基金the Experimental Technology Research Project of Zhejiang University(SYB202138)National Natural Science Foundation of China(32000195)。
文摘With the approval of more and more genetically modified(GM)crops in our country,GM safety management has become more important.Transgenic detection is a major approach for transgenic safety management.Nevertheless,a convenient and visual technique with low equipment requirements and high sensitivity for the field detection of GM plants is still lacking.On the basis of the existing recombinase polymerase amplification(RPA)technique,we developed a multiplex RPA(multi-RPA)method that can simultaneously detect three transgenic elements,including the cauliflower mosaic virus 35S gene(CaMV35S)promoter,neomycin phosphotransferaseⅡgene(NptⅡ)and hygromycin B phosphotransferase gene(Hyg),thus improving the detection rate.Moreover,we coupled this multi-RPA technique with the CRISPR/Cas12a reporter system,which enabled the detection results to be clearly observed by naked eyes under ultraviolet(UV)light(254 nm;which could be achieved by a portable UV flashlight),therefore establishing a multi-RPA visual detection technique.Compared with the traditional test strip detection method,this multi-RPA-CRISPR/Cas12a technique has the higher specificity,higher sensitivity,wider application range and lower cost.Compared with other polymerase chain reaction(PCR)techniques,it also has the advantages of low equipment requirements and visualization,making it a potentially feasible method for the field detection of GM plants.
文摘Cucumber is one of the most important vegetables and economic crops in the world.The occurrence of fungal diseases in cucumbers seriously threatens the safety of cucumber production,with powdery mildew being one of the most common fungal diseases.With the rapid development of computer technology,more and more deep learning algorithms are being applied to identify powdery mildew fungus.However,existing algorithms suffer from low accuracy in recognizing small and occluded targets,as well as insufficient localization precision.To address this issue,the parallelized patch-aware attention(PPA)module was firstly introduced into the backbone network of YOLO v8s.By employing a parallel multi-branch structure and attention mechanism,it effectively captured multi-scale features of small targets,preserved critical information during multiple downsampling processes,and enhanced the performance of small target detection.Additionally,the global-to-local spatial aggregation(GLSA)module was introduced into the neck,which combined global contextual information with local detail features,significantly improving the model’s feature representation capability.This module enhanced the detection performance for small targets and complex scenes by better capturing multi-scale features.Experimental results showed that PG-YOLO v8s significantly improved powdery mildew fungus detection performance compared with YOLO v8s.The network achieved high precision in detecting powdery mildew fungus,with notable improvements in the detection accuracy of small and occluded targets.The research result can provide a high-throughput method for detecting powdery mildew fungus,enabling precise early detection and guiding early intelligent decision-making in cucumber production.This approach can help to improve disease control efficiency,ensure cucumber yield and quality,and it was of great significance for the sustainable development of agricultural production.
文摘[Objective]The accurate identification of maize tassels is critical for the production of hybrid seed.Existing object detection models in complex farmland scenarios face limitations such as restricted data diversity,insufficient feature extraction,high computational load,and low detection efficiency.To address these challenges,a real-time field maize tassel detection model,LightTassel-YOLO(You Only Look Once)based on an improved YOLOv11n is proposed.The model is designed to quickly and accurately identify maize tassels,enabling efficient operation of detasseling unmanned aerial vehicles(UAVs)and reducing the impact of manual intervention.[Methods]Data was continuously collected during the tasseling stage of maize from 2023 to 2024 using UAVs,establishing a large-scale,high-quality maize tassel dataset that covered different maize tasseling stages,multiple varieties,varying altitudes,and diverse meteorological conditions.First,EfficientViT(Efficient vision transformer)was applied as the backbone network to enhance the ability to perceive information across multi-scale features.Second,the C2PSA-CPCA(Convolutional block with parallel spatial attention with channel prior convolutional attention)module was designed to dynamically assign attention weights to the channel and spatial dimensions of feature maps,effectively enhancing the network's capability to extract target features while reducing computational complexity.Finally,the C3k2-SCConv module was constructed to facilitate representative feature learning and achieve low-cost spatial feature reconstruction,thereby improving the model's detection accuracy.[Results and Discussions]The results demonstrated that LightTassel-YOLO provided a reliable method for maize tassel detection.The final model achieved an accuracy of 92.6%,a recall of 89.1%,and an AP@0.5 of 94.7%,representing improvements of 2.5,3.8 and 4.0 percentage points over the baseline model YOLOv11n,respectively.The model had only 3.23 M parameters and a computational cost of 6.7 GFLOPs.In addition,LightTassel-YOLO was compared with mainstream object detection algorithms such as Faster R-CNN,SSD,and multiple versions of the YOLO series.The results demonstrated that the proposed method outperformed these algorithms in overall performance and exhibits excellent adaptability in typical field scenarios.[Conclusions]The proposed method provides an effective theoretical framework for precise maize tassel monitoring and holds significant potential for advancing intelligent field management practices.
文摘To address the challenges of low detection accuracy caused by the diverse species,significant size variations,and complex growth environments of wheat pests in natural settings,a PSA-YOLO11n algorithm is proposed to enhance detection precision.Building upon the YOLO11n framework,the proposed improvements include three key components:1)SimCSPSPPF in Backbone:An improved Spatial Pyramid Pooling-Fast(SPPF)module,SimCSPSPPF,is integrated into the Backbone to reduce the number of channels in the hidden layers,thereby accelerating model training.2)PEC in Neck:The standard convolution layers in the Neck are replaced with Perception Enhancement Convolutions(PEC)to improve multi-scale feature extraction capabilities,enhancing detection speed.3)AWIoU Loss Function:The regression loss function is replaced with Adequate Wise IoU(AWIoU),addressing issues of bounding box distortion caused by the diversity in pest species and size variations,thereby improving the precision of bounding box localization.Experimental evaluations on the IP102 dataset demonstrate that PSA-YOLO11n achieves a mean Average Precision(mAP)of 89.10%,surpassing YOLO11n by 0.8%.Comparisons with other mainstream algorithms,including Faster R-CNN,RetinaNet,YOLOv5s,YOLOv8n,YOLOv10n,and YOLO11n,confirm that PSA-YOLO11n outperforms all baselines in terms of detection performance.These results highlight the algorithm’s capability to significantly improve the detection accuracy of multi-scale wheat pests in natural environments,providing an effective solution for pest management in wheat production.
文摘Human disturbance activities is one of the main reasons for inducing geohazards.Ecological impact assessment metrics of roads are inconsistent criteria and multiple.From the perspective of visual observation,the environment damage can be shown through detecting the uncovered area of vegetation in the images along road.To realize this,an end-to-end environment damage detection model based on convolutional neural network is proposed.A 50-layer residual network is used to extract feature map.The initial parameters are optimized by transfer learning.An example is shown by this method.The dataset including cliff and landslide damage are collected by us along road in Shennongjia national forest park.Results show 0.4703 average precision(AP)rating for cliff damage and 0.4809 average precision(AP)rating for landslide damage.Compared with YOLOv3,our model shows a better accuracy in cliff and landslide detection although a certain amount of speed is sacrificed.
文摘The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.
文摘For the characteristics of small,dense distribution,high diversity of defects and high precision and fast detection in the process of PCB(Printed Circuit Board)defect detection,a defect detection algorithm based on YOLOv8n was proposed in this study.Firstly,the original C2f module of YOLOv8n was improved into a C2FFaster-EMA module to reduce the number of parameters and floating-point operations(FLOPs).Additionally,the WIoUv3 loss function was introduced to mitigate the negative impact of low-quality defect images on model training.Consequently,a reduction in model size and an enhancement in detection precision were achieved.Finally,the ablation and comparative experiments were conducted on an augmented Deep PCB dataset,and the generalization experiments were performed on the PCB Defect-Augmented dataset.The results indicated that the proposed model reduces the number of parameters by 23.3%and FLOPs by 20%,P by 0.7%,mAP@0.5 by 0.3%,and mAP@0.5:0.95 by 3.9%,respectively,compared to the original YOLOv8n model.Furthermore,the comparative experiments demonstrated that the proposed model achieves higher accuracy and mAP compared to YOLOv5n and YOLOv5s.It was concluded that the proposed method satisfies the requirements for both accuracy and speed in PCB defect detection.
基金the funding from Lembaga Penelitian dan Pengabdian Masyarakat(LPPM)Universitas Indonesia,by Riset Kolaborasi Indonesia(RKI)-World Class University(WCU)Program with grant number NKB-1067/UN2-RST/HKP.05.00/2023 and NKB-781/UN2.RST/HKP.05.00/2024.
文摘Detection of target analytes at low concentrations is significant in various fields,including pharmaceuticals,healthcare,and environmental protection.Theophylline(TP),a natural alkaloid used as a bronchodilator to treat respiratory disorders such as asthma,bronchitis,and emphysema,has a narrow therapeutic window with a safe plasma concentration ranging from 55.5-111.0μmol·L^(-1)in adults.Accurate monitoring of TP levels is essential because too low or too high can cause se-rious side effects.In this regard,non-enzymatic electrochemical sensors offer a practical solution with rapidity,portability,and high sensitivity.This article aims to provide a comprehensive review of the recent developments of non-enzymatic electrochemical sensors for TP detection,highlighting the basic principles,electro-oxidation mechanisms,catalytic effects,and the role of modifying materials on electrode performance.Carbon-based electrodes such as glassy carbon electrodes(GCEs),carbon paste electrodes(CPEs),and carbon screen-printed electrodes(SPCEs)have become the primary choices for non-enzymatic sensors due to their chemical stability,low cost,and flexibility in modification.This article identifies the sig-nificant contribution of various modifying materials,including nanomaterials such as carbon nanotubes(CNTs),graphene,metal oxides,and multi-element nanocomposites.These modifications enhance sensors’electron transfer,sensitivity,and selectivity in detecting TP at low concentrations in complex media such as blood plasma and pharmaceutical samples.The electro-oxidation mechanism of TP is also discussed in depth,emphasizing the hydroxyl and carbonyl reaction pathways strongly influenced by pH and electrode materials.These mechanisms guide the selection of the appropriate electrode ma-terial for a particular application.The main contribution of this article is to identify superior modifying materials that can improve the performance of non-enzymatic electrochemical sensors.In a recent study,the combination of multi-element nanocomposites based on titanium dioxide(TiO_(2)),CNTs,and gold nanoparticles(AuNPs)resulted in the lowest detection limit of 3×10^(-5)μmol·L^(-1),reflecting the great potential of these materials for developing high-performance electrochemical sensors.The main conclusion of this article is the importance of a multidisciplinary approach in electrode material design to support the sensitivity and selectivity of TP detection.In addition,there is still a research gap in understanding TP’s more detailed oxidation mechanism,especially under pH variations and complex environments.Therefore,further research on electrode modification and analysis of the TP oxidation mechanism are urgently needed to improve the accuracy and sta-bility of the sensor while expanding its applications in pharmaceutical monitoring and medical diagnostics.By integrating various innovative materials and technical approaches,this review is expected to be an essential reference for developing efficient and affordable non-enzymatic electrochemical sensors.
文摘The abnormal metabolic activity of the tumor can increase the oxygen consumption in tumor cells,and the poor blood perfusion often happens in tumor regions as well,which are the main reasons that result in a hypoxic situation in the tumor.A fluorescence probe,AQD,with selective response toward hypoxia was designed for the detection of hypoxic tumor cells,which was obtained by the covalent connection of a large planar conjugated fluorophore with good fluorescence stability and a N,N-dimethylaniline moiety via the azo bond.The introduction of the azo bond in AQD caused significant fluorescence emission quenching,and the probe was reduced under hypoxic conditions to release the fluorophore via breaking the azo bond,resulting in the gradual recovery of fluorescence emission.Probe AQD exhibited a remarkable fluorescence response in hypoxic conditions,high selectivity,and good biocompatibility,which was successfully used for the imaging of hypoxic tumor cells and realized the detection of hypoxic A549 cells.
文摘Bovine serum albumin(BSA)and glycine(Gly)dual-ligand-modified copper nanoclusters(BSA-Gly CuNCs)with high fluorescence intensity were synthesized by a one-pot strategy.Based on the competitive fluorescence quenching and dynamic quenching effects of ornidazole(ONZ)on BSA-Gly CuNCs,a simple and sensitive detection method for ONZ was successfully developed.The experimental results demonstrate that the addition of the small molecule Gly can more effectively protect CuNCs,and thus enhance its fluorescence intensity and stability.The proposed assay allowed for the detection of ONZ in a linear range of 0.28 to 52.60μmol·L^(-1)and a detection limit of 0.069μmol·L^(-1).Compared with the single-ligand-modified CuNCs,dual-ligand-modified BSA-Gly CuNCs had higher fluorescence intensity,stability,and sensing ability and were successfully applied to evaluate ONZ in actual ONZ tablets.
基金Supported by the Fundamental Research Funds for the Central Universities(2024300443)the Natural Science Foundation of Jiangsu Province(BK20241224).
文摘This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,and a CMOS sensor.In view of the significant contrast between face and background in thermal infra⁃red images,this paper explores a suitable accuracy-latency tradeoff for thermal face detection and proposes a tiny,lightweight detector named YOLO-Fastest-IR.Four YOLO-Fastest-IR models(IR0 to IR3)with different scales are designed based on YOLO-Fastest.To train and evaluate these lightweight models,a multi-user low-resolution thermal face database(RGBT-MLTF)was collected,and the four networks were trained.Experiments demon⁃strate that the lightweight convolutional neural network performs well in thermal infrared face detection tasks.The proposed algorithm outperforms existing face detection methods in both positioning accuracy and speed,making it more suitable for deployment on mobile platforms or embedded devices.After obtaining the region of interest(ROI)in the infrared(IR)image,the RGB camera is guided by the thermal infrared face detection results to achieve fine positioning of the RGB face.Experimental results show that YOLO-Fastest-IR achieves a frame rate of 92.9 FPS on a Raspberry Pi 4B and successfully detects 97.4%of faces in the RGBT-MLTF test set.Ultimate⁃ly,an infrared temperature measurement system with low cost,strong robustness,and high real-time perfor⁃mance was integrated,achieving a temperature measurement accuracy of 0.3℃.
基金National Natural Science Foundation of China(12103020,12363009)Natural Science Foundation of Jiangxi Province(20224BAB211011)+1 种基金Open Project Program of State Key Laboratory of Lunar and Planetary Sciences(Macao University of Science and Technology)(Macao FDCT grant No.002/2024/SKL)Youth Talent Project of Science and Technology Plan of Ganzhou(2022CXRC9191,2023CYZ26970)。
文摘Lunar impact crater detection is crucial for lunar surface studies and spacecraft landing missions,yet deep learning still struggles with accurately detecting small craters,especially when relying on incomplete catalogs.In this work,we integrate Digital Elevation Model(DEM)data to construct a high-quality dataset enriched with slope information,enabling a detailed analysis of crater features and effectively improving detection performance in complex terrains and low-contrast areas.Based on this foundation,we propose a novel two-stage detection network,MSFNet,which leverages multi-scale adaptive feature fusion and multisize ROI pooling to enhance the recognition of craters across various scales.Experimental results demonstrate that MSFNet achieves an F1 score of 74.8%on Test Region1 and a recall rate of 87%for craters with diameters larger than 2 km.Moreover,it shows exceptional performance in detecting sub-kilometer craters by successfully identifying a large number of high-confidence,previously unlabeled targets with a low false detection rate confirmed through manual review.This approach offers an efficient and reliable deep learning solution for lunar impact crater detection.
文摘Two new complexes,[Zn_(2)(L1)(HL1)(NO_(3))]·CH_(3)OH(1)and[Zn_(3)(L2)(L3)_(3)Cl]·CH_(3)OH(2),were successfully synthesized by‘one-pot’method based on cinnoline-3-ylhydrazine ligand and zinc with 2-hydroxy-4-methoxybenzaldehyde and 2-hydroxy-3-methoxybenzaldehyde ligands,respectively,where H_(2)L1=5-methoxy-2-(phthalazin-1-ylhydrazonomethyl)-phenol,H_(2)L2=2-methoxy-6-(phthalazin-1-yl-hydrazonomethyl)-phenol,HL3=2-(1,8-dihydro-[1,2,4]triazolo[3,4-α]phthalazin-3-yl)-6-methoxy-phenol.Complexes 1 and 2 were characterized by infrared spectroscopy,elemental analysis,single-crystal X-ray diffraction,powder X-ray diffraction,etc.It is worth noting that the cinnolin-3-yl-hydrazine ligand and 2-hydroxy-3-methoxybenzaldehyde form two types of Schiff bases(H_(2)L2 and HL3)when in situ reacting and coordinating with Zn(Ⅱ),and HL3 also has two coordination modes.In addition,the fluorescence performance showed that complex 1 can achieve selective and sensitive sensing of Al^(3+)in water with a detection limit of 6.37μmol·L^(-1).CCDC:2413978,1;2413979,2.
文摘In recent years,with the development of synthetic aperture radar(SAR)technology and the widespread application of deep learning,lightweight detection of SAR images has emerged as a research direction.The ultimate goal is to reduce computational and storage requirements while ensuring detection accuracy and reliability,making it an ideal choice for achieving rapid response and efficient processing.In this regard,a lightweight SAR ship target detection algorithm based on YOLOv8 was proposed in this study.Firstly,the C2f-Sc module was designed by fusing the C2f in the backbone network with the ScConv to reduce spatial redundancy and channel redundancy between features in convolutional neural networks.At the same time,the Ghost module was introduced into the neck network to effectively reduce model parameters and computational complexity.A relatively lightweight EMA attention mechanism was added to the neck network to promote the effective fusion of features at different levels.Experimental results showed that the Parameters and GFLOPs of the improved model are reduced by 8.5%and 7.0%when mAP@0.5 and mAP@0.5:0.95 are increased by 0.7%and 1.8%,respectively.It makes the model lightweight and improves the detection accuracy,which has certain application value.
