The YOLOv8 model faces challenges with dense target distribution and small size,resulting in lower accuracy in dense small target detection.To address these issues,an improved small target detection algorithm based on...The YOLOv8 model faces challenges with dense target distribution and small size,resulting in lower accuracy in dense small target detection.To address these issues,an improved small target detection algorithm based on the YOLOv8 model was proposed in this paper.Firstly,the Global Attention Module(GAM)was introduced to enhance data prediction capability and model expression ability.Secondly,the Space-to-Depth(SPD)module was incorporated into the backbone network for fine-grained feature information learning to mitigate feature information loss due to down-sampling.Finally,a 160 pixels×160 pixels feature layer was added to expand small target feature information and effectively reduce instances of missed targets.Experimental validation on the public VisDrone2019 UAV small target detaset demonstrated that the proposed model achieves significant performance improvement in small target detection tasks compared to existing models,exhibiting higher accuracy.展开更多
A gold catalyst of Au/pyrenyl‑graphdiyne(Pyr‑GDY)was prepared by anchoring small size of gold nanoparticles(Au NPs)on the surface of Pyr‑GDY for electrocatalytic nitrogen reduction reaction(eNRR),in which Au NPs with ...A gold catalyst of Au/pyrenyl‑graphdiyne(Pyr‑GDY)was prepared by anchoring small size of gold nanoparticles(Au NPs)on the surface of Pyr‑GDY for electrocatalytic nitrogen reduction reaction(eNRR),in which Au NPs with a size of approximately 3.69 nm was evenly distributed on spongy‑like porous Pyr‑GDY.The catalyst exhibited a good electrocatalytic activity for N_(2)reduction in a nitrogen‑saturated electrolyte,with an ammonia yield of 32.1μg·h^(-1)·mg_(cat)^(-1)at-0.3 V(vs RHE),3.5 times higher than that of Au/C(Au NPs anchored on carbon black).In addition,Au/Pyr‑GDY showed a Faraday efficiency(FE)of 26.9%for eNRR,and a good catalysis durability for over 22 h.展开更多
Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small targe...Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small target detection method based on the tensor nuclear norm and direction residual weighting was proposed.Based on converting the infrared image into an infrared patch tensor model,from the perspective of the low-rank nature of the background tensor,and taking advantage of the difference in contrast between the background and the target in different directions,we designed a double-neighborhood local contrast based on direction residual weighting method(DNLCDRW)combined with the partial sum of tensor nuclear norm(PSTNN)to achieve effective background suppression and recovery of infrared small targets.Experiments show that the algorithm is effective in suppressing the background and improving the detection ability of the target.展开更多
The longitudinal dispersion of the projectile in shooting tests of two-dimensional trajectory corrections fused with fixed canards is extremely large that it sometimes exceeds the correction ability of the correction ...The longitudinal dispersion of the projectile in shooting tests of two-dimensional trajectory corrections fused with fixed canards is extremely large that it sometimes exceeds the correction ability of the correction fuse actuator.The impact point easily deviates from the target,and thus the correction result cannot be readily evaluated.However,the cost of shooting tests is considerably high to conduct many tests for data collection.To address this issue,this study proposes an aiming method for shooting tests based on small sample size.The proposed method uses the Bootstrap method to expand the test data;repeatedly iterates and corrects the position of the simulated theoretical impact points through an improved compatibility test method;and dynamically adjusts the weight of the prior distribution of simulation results based on Kullback-Leibler divergence,which to some extent avoids the real data being"submerged"by the simulation data and achieves the fusion Bayesian estimation of the dispersion center.The experimental results show that when the simulation accuracy is sufficiently high,the proposed method yields a smaller mean-square deviation in estimating the dispersion center and higher shooting accuracy than those of the three comparison methods,which is more conducive to reflecting the effect of the control algorithm and facilitating test personnel to iterate their proposed structures and algorithms.;in addition,this study provides a knowledge base for further comprehensive studies in the future.展开更多
This study presents the ballistic limit velocity of small caliber projectiles against SS400 steel plate derived from live-fire ballistic experiments. Four different small caliber projectiles were tested against SS400 ...This study presents the ballistic limit velocity of small caliber projectiles against SS400 steel plate derived from live-fire ballistic experiments. Four different small caliber projectiles were tested against SS400 steel plates of 9 mm, 10 mm, and 12 mm thicknesses. The ballistic limit velocity was calculated using two standard methods, MIL-STD-662F and NIJ-STD-0101.06, and additionally using a support vector machine algorithm. The results show a linear relationship between the plate thickness and ballistic limit velocity. Further, the relative penetration performance among five different small caliber projectiles was analyzed using the Penetration Performance Ratio(PPR) introduced in this study, which suggests the potential of PPR to predict the ballistic limit velocity of other untested materials and/or different projectiles.展开更多
In order to solve the problems that the current synthetic aperture radar(SAR)image target detection method cannot adapt to targets of different sizes,and the complex image background leads to low detection accuracy,an...In order to solve the problems that the current synthetic aperture radar(SAR)image target detection method cannot adapt to targets of different sizes,and the complex image background leads to low detection accuracy,an improved SAR image small target detection method based on YOLOv7 was proposed in this study.The proposed method improved the feature extraction network by using Switchable Around Convolution(SAConv)in the backbone network to help the model capture target information at different scales,thus improving the feature extraction ability for small targets.Based on the attention mechanism,the DyHead module was embedded in the target detection head to reduce the impact of complex background,and better focus on the small targets.In addition,the NWD loss function was introduced and combined with CIoU loss.Compared to the CIoU loss function typically used in YOLOv7,the NWD loss function pays more attention to the processing of small targets,so as to further improve the detection ability of small targets.The experimental results on the HRSID dataset indicate that the proposed method achieved mAP@0.5 and mAP@0.95 scores of 93.5%and 71.5%,respectively.Compared to the baseline model,this represents an increase of 7.2%and 7.6%,respectively.The proposed method can effectively complete the task of SAR image small target detection.展开更多
文摘The YOLOv8 model faces challenges with dense target distribution and small size,resulting in lower accuracy in dense small target detection.To address these issues,an improved small target detection algorithm based on the YOLOv8 model was proposed in this paper.Firstly,the Global Attention Module(GAM)was introduced to enhance data prediction capability and model expression ability.Secondly,the Space-to-Depth(SPD)module was incorporated into the backbone network for fine-grained feature information learning to mitigate feature information loss due to down-sampling.Finally,a 160 pixels×160 pixels feature layer was added to expand small target feature information and effectively reduce instances of missed targets.Experimental validation on the public VisDrone2019 UAV small target detaset demonstrated that the proposed model achieves significant performance improvement in small target detection tasks compared to existing models,exhibiting higher accuracy.
文摘A gold catalyst of Au/pyrenyl‑graphdiyne(Pyr‑GDY)was prepared by anchoring small size of gold nanoparticles(Au NPs)on the surface of Pyr‑GDY for electrocatalytic nitrogen reduction reaction(eNRR),in which Au NPs with a size of approximately 3.69 nm was evenly distributed on spongy‑like porous Pyr‑GDY.The catalyst exhibited a good electrocatalytic activity for N_(2)reduction in a nitrogen‑saturated electrolyte,with an ammonia yield of 32.1μg·h^(-1)·mg_(cat)^(-1)at-0.3 V(vs RHE),3.5 times higher than that of Au/C(Au NPs anchored on carbon black).In addition,Au/Pyr‑GDY showed a Faraday efficiency(FE)of 26.9%for eNRR,and a good catalysis durability for over 22 h.
基金Supported by the Key Laboratory Fund for Equipment Pre-Research(6142207210202)。
文摘Aiming at the problem that infrared small target detection faces low contrast between the background and the target and insufficient noise suppression ability under the complex cloud background,an infrared small target detection method based on the tensor nuclear norm and direction residual weighting was proposed.Based on converting the infrared image into an infrared patch tensor model,from the perspective of the low-rank nature of the background tensor,and taking advantage of the difference in contrast between the background and the target in different directions,we designed a double-neighborhood local contrast based on direction residual weighting method(DNLCDRW)combined with the partial sum of tensor nuclear norm(PSTNN)to achieve effective background suppression and recovery of infrared small targets.Experiments show that the algorithm is effective in suppressing the background and improving the detection ability of the target.
基金the National Natural Science Foundation of China(Grant No.61973033)Preliminary Research of Equipment(Grant No.9090102010305)for funding the experiments。
文摘The longitudinal dispersion of the projectile in shooting tests of two-dimensional trajectory corrections fused with fixed canards is extremely large that it sometimes exceeds the correction ability of the correction fuse actuator.The impact point easily deviates from the target,and thus the correction result cannot be readily evaluated.However,the cost of shooting tests is considerably high to conduct many tests for data collection.To address this issue,this study proposes an aiming method for shooting tests based on small sample size.The proposed method uses the Bootstrap method to expand the test data;repeatedly iterates and corrects the position of the simulated theoretical impact points through an improved compatibility test method;and dynamically adjusts the weight of the prior distribution of simulation results based on Kullback-Leibler divergence,which to some extent avoids the real data being"submerged"by the simulation data and achieves the fusion Bayesian estimation of the dispersion center.The experimental results show that when the simulation accuracy is sufficiently high,the proposed method yields a smaller mean-square deviation in estimating the dispersion center and higher shooting accuracy than those of the three comparison methods,which is more conducive to reflecting the effect of the control algorithm and facilitating test personnel to iterate their proposed structures and algorithms.;in addition,this study provides a knowledge base for further comprehensive studies in the future.
文摘This study presents the ballistic limit velocity of small caliber projectiles against SS400 steel plate derived from live-fire ballistic experiments. Four different small caliber projectiles were tested against SS400 steel plates of 9 mm, 10 mm, and 12 mm thicknesses. The ballistic limit velocity was calculated using two standard methods, MIL-STD-662F and NIJ-STD-0101.06, and additionally using a support vector machine algorithm. The results show a linear relationship between the plate thickness and ballistic limit velocity. Further, the relative penetration performance among five different small caliber projectiles was analyzed using the Penetration Performance Ratio(PPR) introduced in this study, which suggests the potential of PPR to predict the ballistic limit velocity of other untested materials and/or different projectiles.
文摘In order to solve the problems that the current synthetic aperture radar(SAR)image target detection method cannot adapt to targets of different sizes,and the complex image background leads to low detection accuracy,an improved SAR image small target detection method based on YOLOv7 was proposed in this study.The proposed method improved the feature extraction network by using Switchable Around Convolution(SAConv)in the backbone network to help the model capture target information at different scales,thus improving the feature extraction ability for small targets.Based on the attention mechanism,the DyHead module was embedded in the target detection head to reduce the impact of complex background,and better focus on the small targets.In addition,the NWD loss function was introduced and combined with CIoU loss.Compared to the CIoU loss function typically used in YOLOv7,the NWD loss function pays more attention to the processing of small targets,so as to further improve the detection ability of small targets.The experimental results on the HRSID dataset indicate that the proposed method achieved mAP@0.5 and mAP@0.95 scores of 93.5%and 71.5%,respectively.Compared to the baseline model,this represents an increase of 7.2%and 7.6%,respectively.The proposed method can effectively complete the task of SAR image small target detection.
