The subcortical visual pathway is generally thought to be involved in dangerous information processing,such as fear processing and defensive behavior.A recent study,published in Human Brain Mapping,shows a new functio...The subcortical visual pathway is generally thought to be involved in dangerous information processing,such as fear processing and defensive behavior.A recent study,published in Human Brain Mapping,shows a new function of the subcortical pathway involved in the fast processing of non-emotional object perception.Rapid object processing is a critical function of visual system.Topological perception theory proposes that the initial perception of objects begins with the extraction of topological property(TP).However,the mechanism of rapid TP processing remains unclear.The researchers investigated the subcortical mechanism of TP processing with transcranial magnetic stimulation(TMS).They find that a subcortical magnocellular pathway is responsible for the early processing of TP,and this subcortical processing of TP accelerates object recognition.Based on their findings,we propose a novel training approach called subcortical magnocellular pathway training(SMPT),aimed at improving the efficiency of the subcortical M pathway to restore visual and attentional functions in disorders associated with subcortical pathway dysfunction.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
Objective Autism spectrum disorder(ASD)is a neurodevelopmental condition characterized by difficulties with communication and social interaction,restricted and repetitive behaviors.Previous studies have indicated that...Objective Autism spectrum disorder(ASD)is a neurodevelopmental condition characterized by difficulties with communication and social interaction,restricted and repetitive behaviors.Previous studies have indicated that individuals with ASD exhibit early and lifelong attention deficits,which are closely related to the core symptoms of ASD.Basic visual attention processes may provide a critical foundation for their social communication and interaction abilities.Therefore,this study explores the behavior of children with ASD in capturing attention to changes in topological properties.Methods Our study recruited twenty-seven ASD children diagnosed by professional clinicians according to DSM-5 and twenty-eight typically developing(TD)age-matched controls.In an attention capture task,we recorded the saccadic behaviors of children with ASD and TD in response to topological change(TC)and non-topological change(nTC)stimuli.Saccadic reaction time(SRT),visual search time(VS),and first fixation dwell time(FFDT)were used as indicators of attentional bias.Pearson correlation tests between the clinical assessment scales and attentional bias were conducted.Results This study found that TD children had significantly faster SRT(P<0.05)and VS(P<0.05)for the TC stimuli compared to the nTC stimuli,while the children with ASD did not exhibit significant differences in either measure(P>0.05).Additionally,ASD children demonstrated significantly less attention towards the TC targets(measured by FFDT),in comparison to TD children(P<0.05).Furthermore,ASD children exhibited a significant negative linear correlation between their attentional bias(measured by VS)and their scores on the compulsive subscale(P<0.05).Conclusion The results suggest that children with ASD have difficulty shifting their attention to objects with topological changes during change detection.This atypical attention may affect the child’s cognitive and behavioral development,thereby impacting their social communication and interaction.In sum,our findings indicate that difficulties in attentional capture by TC may be a key feature of ASD.展开更多
If we take Chinese modern literary history( female) as the"center",in which the female creation is very limited,a large amount of female compositions in late Qing Dynasty and R epublican period were "ma...If we take Chinese modern literary history( female) as the"center",in which the female creation is very limited,a large amount of female compositions in late Qing Dynasty and R epublican period were "marginalized",even in the "hidden "state. These "marginalized"female writings,not only are in a large number,but also constitute the foundation of Chinese modern female literature creation,the significance and value cannot be ignored. To seek the strategy and approach for the research on the "marginalized"female creation in late Qing Dynasty and R epublican period,helps to deeply discuss the possibility to expand the object scope of female literature research,to find a newgrowing point of female literature research,and form conversation between female literary history writing and current theory and practice of the"R ewriting Literary History".展开更多
为解决YOLOv8n算法在机场异物检测中存在计算复杂度高、计算资源消耗大的问题,通过在YOLOv8n算法中引入轻量化模块的方法研究了机场异物检测的问题,提出了Fast-BiYOLOv8n算法。首先,设计了C2f_FasterEMA模块并引入YOLOv8n算法的骨干网络...为解决YOLOv8n算法在机场异物检测中存在计算复杂度高、计算资源消耗大的问题,通过在YOLOv8n算法中引入轻量化模块的方法研究了机场异物检测的问题,提出了Fast-BiYOLOv8n算法。首先,设计了C2f_FasterEMA模块并引入YOLOv8n算法的骨干网络中,该模块融合了FasterBlock模块和高效多尺度注意力(efficient multi-scale attention,EMA)注意力机制,增强了图像的特征提取能力,同时降低了算法计算量;其次,在路径聚合网络(path aggregation network,PANet,)网络架构中融合了骨干网络中的P2特征层并设计了双向特征金字塔网络(bidirectional feature pyramid network,BiFPN)网络架构,增加了跨尺度连接促进了不同特征图之间的信息融合,同时加入C2f_Faster模块提高了特征融合的效率并进一步降低了算法的计算量;最后,通过改进损失函数为Inner-CIoU(intersection over union,complete intersection over union loss)加快了算法的收敛速度,提高了检测准确率。结果表明,Fast-BiYOLOv8n算法的检测准确率达到99.0%,召回率为98.8%,平均精度均值(mean average precision,mAP)提升了3.5个百分点,达到99.3%,参数量比原模型降低了27%,模型的权重大小降低了21%,实现了在降低算法参数量的同时,提升检测准确率的目的。展开更多
A method for moving object recognition and tracking in the intelligent traffic monitoring system is presented. For the shortcomings and deficiencies of the frame-subtraction method, a redundant discrete wavelet transf...A method for moving object recognition and tracking in the intelligent traffic monitoring system is presented. For the shortcomings and deficiencies of the frame-subtraction method, a redundant discrete wavelet transform （RDWT） based moving object recognition algorithm is put forward, which directly detects moving objects in the redundant discrete wavelet transform domain. An improved adaptive mean-shift algorithm is used to track the moving object in the follow up frames. Experimental results show that the algorithm can effectively extract the moving object, even though the object is similar to the background, and the results are better than the traditional frame-subtraction method. The object tracking is accurate without the impact of changes in the size of the object. Therefore the algorithm has a certain practical value and prospect.展开更多
The decomposition based approach decomposes a multi-objective problem into a series of single objective subproblems, which are optimized along contours towards the ideal point. But non-dominated solutions cannot sprea...The decomposition based approach decomposes a multi-objective problem into a series of single objective subproblems, which are optimized along contours towards the ideal point. But non-dominated solutions cannot spread uniformly, since the Pareto front shows different features, such as concave and convex. To improve the distribution uniformity of non-dominated solutions, a bidirectional decomposition based approach that constructs two search directions is proposed to provide a uniform distribution no matter what features problems have. Since two populations along two search directions show differently on diversity and convergence, an adaptive neighborhood selection approach is presented to choose suitable parents for the offspring generation. In order to avoid the problem of the shrinking search region caused by the close distance of the ideal and nadir points, a reference point update approach is presented. The performance of the proposed algorithm is validated with four state-of-the-art algorithms. Experimental results demonstrate the superiority of the proposed algorithm on all considered test problems.展开更多
With the increasing number of vehicles,manual security inspections are becoming more laborious at road checkpoints.To address it,a specialized Road Checkpoints Robot(RCRo)system is proposed,incorporated with enhanced ...With the increasing number of vehicles,manual security inspections are becoming more laborious at road checkpoints.To address it,a specialized Road Checkpoints Robot(RCRo)system is proposed,incorporated with enhanced You Only Look Once(YOLO)and a 6-degree-of-freedom(DOF)manipulator,for autonomous identity verification and vehicle inspection.The modified YOLO is characterized by large objects’sensitivity and faster detection speed,named“LF-YOLO”.The better sensitivity of large objects and the faster detection speed are achieved by means of the Dense module-based backbone network connecting two-scale detecting network,for object detection tasks,along with optimized anchor boxes and improved loss function.During the manipulator motion,Octree-aided motion control scheme is adopted for collision-free motion through Robot Operating System(ROS).The proposed LF-YOLO which utilizes continuous optimization strategy and residual technique provides a promising detector design,which has been found to be more effective during actual object detection,in terms of decreased average detection time by 68.25%and 60.60%,and increased average Intersection over Union(Io U)by 20.74%and6.79%compared to YOLOv3 and YOLOv4 through experiments.The comprehensive functional tests of RCRo system demonstrate the feasibility and competency of the multiple unmanned inspections in practice.展开更多
Moving object extraction and classification are important problems in automated video surveillance systems. A background model based on region segmentation is proposed. An adaptive single Gaussian background model is ...Moving object extraction and classification are important problems in automated video surveillance systems. A background model based on region segmentation is proposed. An adaptive single Gaussian background model is used in the stable region with gradual changes, and a nonparametric model is used in the variable region with jumping changes. A generalized agglomerative scheme is used to merge the pixels in the variable region and fill in the small interspaces. A two-threshold sequential algorithmic scheme is used to group the background samples of the variable region into distinct Gaussian distributions to accelerate the kernel density computation speed of the nonparametric model. In the feature-based object classification phase, the surveillance scene is first partitioned according to the road boundaries of different traffic directions and then re-segmented according to their scene localities. The method improves the discriminability of the features in each partition. AdaBoost method is applied to evaluate the relative importance of the features in each partition respectively and distinguish whether an object is a vehicle, a single human, a human group, or a bike. Experimental results show that the proposed method achieves higher performance in comparison with the existing method.展开更多
In the study of oriented bounding boxes(OBB)object detection in high-resolution remote sensing images,the problem of missed and wrong detection of small targets occurs because the targets are too small and have differ...In the study of oriented bounding boxes(OBB)object detection in high-resolution remote sensing images,the problem of missed and wrong detection of small targets occurs because the targets are too small and have different orientations.Existing OBB object detection for remote sensing images,although making good progress,mainly focuses on directional modeling,while less consideration is given to the size of the object as well as the problem of missed detection.In this study,a method based on improved YOLOv8 was proposed for detecting oriented objects in remote sensing images,which can improve the detection precision of oriented objects in remote sensing images.Firstly,the ResCBAMG module was innovatively designed,which could better extract channel and spatial correlation information.Secondly,the innovative top-down feature fusion layer network structure was proposed in conjunction with the Efficient Channel Attention(ECA)attention module,which helped to capture inter-local cross-channel interaction information appropriately.Finally,we introduced an innovative ResCBAMG module between the different C2f modules and detection heads of the bottom-up feature fusion layer.This innovative structure helped the model to better focus on the target area.The precision and robustness of oriented target detection were also improved.Experimental results on the DOTA-v1.5 dataset showed that the detection Precision,mAP@0.5,and mAP@0.5:0.95 metrics of the improved model are better compared to the original model.This improvement is effective in detecting small targets and complex scenes.展开更多
During a sea firing training,the intelligent detection of projectile-induced water column targets in a firing video is the prerequisite for and critical to the automatic calculation of miss distance,while the correct ...During a sea firing training,the intelligent detection of projectile-induced water column targets in a firing video is the prerequisite for and critical to the automatic calculation of miss distance,while the correct and precise calculation of miss distance is directly affected by the accuracy,false alarm rate and time delay of detection.After analyzing the characteristics of projectile-induced water columns,an accurate detection algorithm for time backtracked projectile-induced water columns based on the improved you only look once(YOLO)network is put forward.The capability and accuracy of detecting projectileinduced water column targets with the conventional YOLO network are improved by optimizing the anchor box through K-means clustering and embedding the squeeze and excitation(SE)attention module.The detection area is limited by adopting a sea-sky line detection algorithm based on gray level co-occurrence matrix(GLCM),so as to effectively eliminate such disturbances as ocean waves and ship wakes,and lower the false alarm rate of projectile-induced water column detection.The improved algorithm increases the mAP50 of water column detection by 30.3%.On the basis of correct detection,a time backtracking algorithm is designed with mean shift to track images containing projectile-induced water column in reverse time sequence.It accurately detects a projectile-induced water column at the time of its initial appearance as well as its pixel position in images,and considerably reduces detection delay,so as to provide the support for the automatic,accurate,and real-time calculation of miss distance.展开更多
文摘The subcortical visual pathway is generally thought to be involved in dangerous information processing,such as fear processing and defensive behavior.A recent study,published in Human Brain Mapping,shows a new function of the subcortical pathway involved in the fast processing of non-emotional object perception.Rapid object processing is a critical function of visual system.Topological perception theory proposes that the initial perception of objects begins with the extraction of topological property(TP).However,the mechanism of rapid TP processing remains unclear.The researchers investigated the subcortical mechanism of TP processing with transcranial magnetic stimulation(TMS).They find that a subcortical magnocellular pathway is responsible for the early processing of TP,and this subcortical processing of TP accelerates object recognition.Based on their findings,we propose a novel training approach called subcortical magnocellular pathway training(SMPT),aimed at improving the efficiency of the subcortical M pathway to restore visual and attentional functions in disorders associated with subcortical pathway dysfunction.
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Objective Autism spectrum disorder(ASD)is a neurodevelopmental condition characterized by difficulties with communication and social interaction,restricted and repetitive behaviors.Previous studies have indicated that individuals with ASD exhibit early and lifelong attention deficits,which are closely related to the core symptoms of ASD.Basic visual attention processes may provide a critical foundation for their social communication and interaction abilities.Therefore,this study explores the behavior of children with ASD in capturing attention to changes in topological properties.Methods Our study recruited twenty-seven ASD children diagnosed by professional clinicians according to DSM-5 and twenty-eight typically developing(TD)age-matched controls.In an attention capture task,we recorded the saccadic behaviors of children with ASD and TD in response to topological change(TC)and non-topological change(nTC)stimuli.Saccadic reaction time(SRT),visual search time(VS),and first fixation dwell time(FFDT)were used as indicators of attentional bias.Pearson correlation tests between the clinical assessment scales and attentional bias were conducted.Results This study found that TD children had significantly faster SRT(P<0.05)and VS(P<0.05)for the TC stimuli compared to the nTC stimuli,while the children with ASD did not exhibit significant differences in either measure(P>0.05).Additionally,ASD children demonstrated significantly less attention towards the TC targets(measured by FFDT),in comparison to TD children(P<0.05).Furthermore,ASD children exhibited a significant negative linear correlation between their attentional bias(measured by VS)and their scores on the compulsive subscale(P<0.05).Conclusion The results suggest that children with ASD have difficulty shifting their attention to objects with topological changes during change detection.This atypical attention may affect the child’s cognitive and behavioral development,thereby impacting their social communication and interaction.In sum,our findings indicate that difficulties in attentional capture by TC may be a key feature of ASD.
基金Supported by National Basic Research Program of China（973 Program）（2013CB035500） National Natural Science Foundation of China（61233004,61221003,61074061）+1 种基金 International Cooperation Program of Shanghai Science and Technology Commission （12230709600） the Higher Education Research Fund for the Doctoral Program of China（20120073130006）
文摘If we take Chinese modern literary history( female) as the"center",in which the female creation is very limited,a large amount of female compositions in late Qing Dynasty and R epublican period were "marginalized",even in the "hidden "state. These "marginalized"female writings,not only are in a large number,but also constitute the foundation of Chinese modern female literature creation,the significance and value cannot be ignored. To seek the strategy and approach for the research on the "marginalized"female creation in late Qing Dynasty and R epublican period,helps to deeply discuss the possibility to expand the object scope of female literature research,to find a newgrowing point of female literature research,and form conversation between female literary history writing and current theory and practice of the"R ewriting Literary History".
文摘为解决YOLOv8n算法在机场异物检测中存在计算复杂度高、计算资源消耗大的问题,通过在YOLOv8n算法中引入轻量化模块的方法研究了机场异物检测的问题,提出了Fast-BiYOLOv8n算法。首先,设计了C2f_FasterEMA模块并引入YOLOv8n算法的骨干网络中,该模块融合了FasterBlock模块和高效多尺度注意力(efficient multi-scale attention,EMA)注意力机制,增强了图像的特征提取能力,同时降低了算法计算量;其次,在路径聚合网络(path aggregation network,PANet,)网络架构中融合了骨干网络中的P2特征层并设计了双向特征金字塔网络(bidirectional feature pyramid network,BiFPN)网络架构,增加了跨尺度连接促进了不同特征图之间的信息融合,同时加入C2f_Faster模块提高了特征融合的效率并进一步降低了算法的计算量;最后,通过改进损失函数为Inner-CIoU(intersection over union,complete intersection over union loss)加快了算法的收敛速度,提高了检测准确率。结果表明,Fast-BiYOLOv8n算法的检测准确率达到99.0%,召回率为98.8%,平均精度均值(mean average precision,mAP)提升了3.5个百分点,达到99.3%,参数量比原模型降低了27%,模型的权重大小降低了21%,实现了在降低算法参数量的同时,提升检测准确率的目的。
文摘A method for moving object recognition and tracking in the intelligent traffic monitoring system is presented. For the shortcomings and deficiencies of the frame-subtraction method, a redundant discrete wavelet transform （RDWT） based moving object recognition algorithm is put forward, which directly detects moving objects in the redundant discrete wavelet transform domain. An improved adaptive mean-shift algorithm is used to track the moving object in the follow up frames. Experimental results show that the algorithm can effectively extract the moving object, even though the object is similar to the background, and the results are better than the traditional frame-subtraction method. The object tracking is accurate without the impact of changes in the size of the object. Therefore the algorithm has a certain practical value and prospect.
文摘The decomposition based approach decomposes a multi-objective problem into a series of single objective subproblems, which are optimized along contours towards the ideal point. But non-dominated solutions cannot spread uniformly, since the Pareto front shows different features, such as concave and convex. To improve the distribution uniformity of non-dominated solutions, a bidirectional decomposition based approach that constructs two search directions is proposed to provide a uniform distribution no matter what features problems have. Since two populations along two search directions show differently on diversity and convergence, an adaptive neighborhood selection approach is presented to choose suitable parents for the offspring generation. In order to avoid the problem of the shrinking search region caused by the close distance of the ideal and nadir points, a reference point update approach is presented. The performance of the proposed algorithm is validated with four state-of-the-art algorithms. Experimental results demonstrate the superiority of the proposed algorithm on all considered test problems.
基金supported by the National Key Research and Development Program of China(grant number:2017YFC0806503)。
文摘With the increasing number of vehicles,manual security inspections are becoming more laborious at road checkpoints.To address it,a specialized Road Checkpoints Robot(RCRo)system is proposed,incorporated with enhanced You Only Look Once(YOLO)and a 6-degree-of-freedom(DOF)manipulator,for autonomous identity verification and vehicle inspection.The modified YOLO is characterized by large objects’sensitivity and faster detection speed,named“LF-YOLO”.The better sensitivity of large objects and the faster detection speed are achieved by means of the Dense module-based backbone network connecting two-scale detecting network,for object detection tasks,along with optimized anchor boxes and improved loss function.During the manipulator motion,Octree-aided motion control scheme is adopted for collision-free motion through Robot Operating System(ROS).The proposed LF-YOLO which utilizes continuous optimization strategy and residual technique provides a promising detector design,which has been found to be more effective during actual object detection,in terms of decreased average detection time by 68.25%and 60.60%,and increased average Intersection over Union(Io U)by 20.74%and6.79%compared to YOLOv3 and YOLOv4 through experiments.The comprehensive functional tests of RCRo system demonstrate the feasibility and competency of the multiple unmanned inspections in practice.
基金supported by the Science and Technology Program of Zhejiang Province of China(2005C11001-02).
文摘Moving object extraction and classification are important problems in automated video surveillance systems. A background model based on region segmentation is proposed. An adaptive single Gaussian background model is used in the stable region with gradual changes, and a nonparametric model is used in the variable region with jumping changes. A generalized agglomerative scheme is used to merge the pixels in the variable region and fill in the small interspaces. A two-threshold sequential algorithmic scheme is used to group the background samples of the variable region into distinct Gaussian distributions to accelerate the kernel density computation speed of the nonparametric model. In the feature-based object classification phase, the surveillance scene is first partitioned according to the road boundaries of different traffic directions and then re-segmented according to their scene localities. The method improves the discriminability of the features in each partition. AdaBoost method is applied to evaluate the relative importance of the features in each partition respectively and distinguish whether an object is a vehicle, a single human, a human group, or a bike. Experimental results show that the proposed method achieves higher performance in comparison with the existing method.
文摘In the study of oriented bounding boxes(OBB)object detection in high-resolution remote sensing images,the problem of missed and wrong detection of small targets occurs because the targets are too small and have different orientations.Existing OBB object detection for remote sensing images,although making good progress,mainly focuses on directional modeling,while less consideration is given to the size of the object as well as the problem of missed detection.In this study,a method based on improved YOLOv8 was proposed for detecting oriented objects in remote sensing images,which can improve the detection precision of oriented objects in remote sensing images.Firstly,the ResCBAMG module was innovatively designed,which could better extract channel and spatial correlation information.Secondly,the innovative top-down feature fusion layer network structure was proposed in conjunction with the Efficient Channel Attention(ECA)attention module,which helped to capture inter-local cross-channel interaction information appropriately.Finally,we introduced an innovative ResCBAMG module between the different C2f modules and detection heads of the bottom-up feature fusion layer.This innovative structure helped the model to better focus on the target area.The precision and robustness of oriented target detection were also improved.Experimental results on the DOTA-v1.5 dataset showed that the detection Precision,mAP@0.5,and mAP@0.5:0.95 metrics of the improved model are better compared to the original model.This improvement is effective in detecting small targets and complex scenes.
基金supported by the National Natural Science Foundation of China(51679247)。
文摘During a sea firing training,the intelligent detection of projectile-induced water column targets in a firing video is the prerequisite for and critical to the automatic calculation of miss distance,while the correct and precise calculation of miss distance is directly affected by the accuracy,false alarm rate and time delay of detection.After analyzing the characteristics of projectile-induced water columns,an accurate detection algorithm for time backtracked projectile-induced water columns based on the improved you only look once(YOLO)network is put forward.The capability and accuracy of detecting projectileinduced water column targets with the conventional YOLO network are improved by optimizing the anchor box through K-means clustering and embedding the squeeze and excitation(SE)attention module.The detection area is limited by adopting a sea-sky line detection algorithm based on gray level co-occurrence matrix(GLCM),so as to effectively eliminate such disturbances as ocean waves and ship wakes,and lower the false alarm rate of projectile-induced water column detection.The improved algorithm increases the mAP50 of water column detection by 30.3%.On the basis of correct detection,a time backtracking algorithm is designed with mean shift to track images containing projectile-induced water column in reverse time sequence.It accurately detects a projectile-induced water column at the time of its initial appearance as well as its pixel position in images,and considerably reduces detection delay,so as to provide the support for the automatic,accurate,and real-time calculation of miss distance.
