Based on the coded and non-coded targets, the targets are extracted from the images according to their size, shape and intensity etc., and thus an improved method to identify the unique identity（D） of every coded ta...Based on the coded and non-coded targets, the targets are extracted from the images according to their size, shape and intensity etc., and thus an improved method to identify the unique identity（D） of every coded target is put forward and the non-coded and coded targets are classified. Moreover, the gray scale centroid algorithm is applied to obtain the subpixel location of both uncoded and coded targets. The initial matching of the uncoded target correspondences between an image pair is established according to similarity and compatibility, which are based on the ID correspondences of the coded targets. The outliers in the initial matching of the uncoded target are eliminated according to three rules to finally obtain the uncoded target correspondences. Practical examples show that the algorithm is rapid, robust and is of high precision and matching ratio.展开更多
Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method ...Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo(SMC)-based probability hypothesis density(PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.展开更多
For the linear crack skeleton of railway bridges with irregular strike,it is difficult to accurately express the crack contour feature by using a single smoothing fitting algorithm.In order to improve the measurement ...For the linear crack skeleton of railway bridges with irregular strike,it is difficult to accurately express the crack contour feature by using a single smoothing fitting algorithm.In order to improve the measurement accuracy,a polynomial curve fitting was proposed,which used the calibration point of crack contour as the boundary point,and then put them all together to produce a continuous contour curve to achieve the crack length measurement.The method was tested by measuring the linar cracks with different shapes.It is shown that this proposed algorithm can not only solve the jagged problem generated in the crack skeleton extraction process,but also improve the crack length measurement accuracy.The relative deviation is less than 0.15,and the measurement accuracy is over 98.05%,which provides a more effective means for the crack length measurement in railway bridges.展开更多
Single object tracking based on deep learning has achieved the advanced performance in many applications of computer vision.However,the existing trackers have certain limitations owing to deformation,occlusion,movemen...Single object tracking based on deep learning has achieved the advanced performance in many applications of computer vision.However,the existing trackers have certain limitations owing to deformation,occlusion,movement and some other conditions.We propose a siamese attentional dense network called SiamADN in an end-to-end offline manner,especially aiming at unmanned aerial vehicle(UAV)tracking.First,it applies a dense network to reduce vanishing-gradient,which strengthens the features transfer.Second,the channel attention mechanism is involved into the Densenet structure,in order to focus on the possible key regions.The advance corner detection network is introduced to improve the following tracking process.Extensive experiments are carried out on four mainly tracking benchmarks as OTB-2015,UAV123,LaSOT and VOT.The accuracy rate on UAV123 is 78.9%,and the running speed is 32 frame per second(FPS),which demonstrates its efficiency in the practical real application.展开更多
For the detection and tracking of dim point targets with SNR 〈 2 dB, the combined SPRT and FSS method is given to accomplish detection in whicb likelihood testing are carried out twice to prune constantly. Firstly, t...For the detection and tracking of dim point targets with SNR 〈 2 dB, the combined SPRT and FSS method is given to accomplish detection in whicb likelihood testing are carried out twice to prune constantly. Firstly, the SPRT is developed aiming at the heuristic segments formed by correlation analysis. In order to avoid missing detection the threshold is chosen much lower. Secondly, by adding samples and choosing the one most similar to the heuristic segment to make state estimation FSS is implemented. This time we choose a higher threshold. Moreover in preprocessing the compound kernel estimation is designed to depress varying background clutter. Multiple experimental sequences validate that the method is more suitable for the dim targets detection and tracking compared with the scheme choosing the higher intensity pixel in tracking. It not only has perfect detection performance but also can greatly enhance tracking performance.展开更多
Co-occurrence matrices have been successfully applied in texture classification and segmentation.However,they have poor computation performance in real-time application.In this paper,the efficient co-occurrence matrix...Co-occurrence matrices have been successfully applied in texture classification and segmentation.However,they have poor computation performance in real-time application.In this paper,the efficient co-occurrence matrix solution for defect detection is focused on,and a method of Fuzzy Label Co-occurrence Matrix (FLCM) set is proposed.In this method,all gray levels are supposed to subject to some fuzzy sets called fuzzy tonal sets and three defective features are defined.Features of FLCM set with various parameters are combined for the final judgment.Unlike many methods,image acquired for learning hasn't to be entirely free of defects.It is shown that the method produces high accuracy and can be a competent candidate for plain colour fabric defect detection.展开更多
基金The National Natural Science Foundation of China(No50475041)
文摘Based on the coded and non-coded targets, the targets are extracted from the images according to their size, shape and intensity etc., and thus an improved method to identify the unique identity（D） of every coded target is put forward and the non-coded and coded targets are classified. Moreover, the gray scale centroid algorithm is applied to obtain the subpixel location of both uncoded and coded targets. The initial matching of the uncoded target correspondences between an image pair is established according to similarity and compatibility, which are based on the ID correspondences of the coded targets. The outliers in the initial matching of the uncoded target are eliminated according to three rules to finally obtain the uncoded target correspondences. Practical examples show that the algorithm is rapid, robust and is of high precision and matching ratio.
基金Projects(61002022,61471370)supported by the National Natural Science Foundation of China
文摘Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo(SMC)-based probability hypothesis density(PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.
基金National Defense Pre-Research Fund Project(No.060601)Wanqiao Education Fund Project(No.06010023)。
文摘For the linear crack skeleton of railway bridges with irregular strike,it is difficult to accurately express the crack contour feature by using a single smoothing fitting algorithm.In order to improve the measurement accuracy,a polynomial curve fitting was proposed,which used the calibration point of crack contour as the boundary point,and then put them all together to produce a continuous contour curve to achieve the crack length measurement.The method was tested by measuring the linar cracks with different shapes.It is shown that this proposed algorithm can not only solve the jagged problem generated in the crack skeleton extraction process,but also improve the crack length measurement accuracy.The relative deviation is less than 0.15,and the measurement accuracy is over 98.05%,which provides a more effective means for the crack length measurement in railway bridges.
基金supported by the Zhejiang Key Laboratory of General Aviation Operation Technology(No.JDGA2020-7)the National Natural Science Foundation of China(No.62173237)+3 种基金the Natural Science Foundation of Liaoning Province(No.2019-MS-251)the Talent Project of Revitalization Liaoning Province(No.XLYC1907022)the Key R&D Projects of Liaoning Province(No.2020JH2/10100045)the High-Level Innovation Talent Project of Shenyang(No.RC190030).
文摘Single object tracking based on deep learning has achieved the advanced performance in many applications of computer vision.However,the existing trackers have certain limitations owing to deformation,occlusion,movement and some other conditions.We propose a siamese attentional dense network called SiamADN in an end-to-end offline manner,especially aiming at unmanned aerial vehicle(UAV)tracking.First,it applies a dense network to reduce vanishing-gradient,which strengthens the features transfer.Second,the channel attention mechanism is involved into the Densenet structure,in order to focus on the possible key regions.The advance corner detection network is introduced to improve the following tracking process.Extensive experiments are carried out on four mainly tracking benchmarks as OTB-2015,UAV123,LaSOT and VOT.The accuracy rate on UAV123 is 78.9%,and the running speed is 32 frame per second(FPS),which demonstrates its efficiency in the practical real application.
文摘For the detection and tracking of dim point targets with SNR 〈 2 dB, the combined SPRT and FSS method is given to accomplish detection in whicb likelihood testing are carried out twice to prune constantly. Firstly, the SPRT is developed aiming at the heuristic segments formed by correlation analysis. In order to avoid missing detection the threshold is chosen much lower. Secondly, by adding samples and choosing the one most similar to the heuristic segment to make state estimation FSS is implemented. This time we choose a higher threshold. Moreover in preprocessing the compound kernel estimation is designed to depress varying background clutter. Multiple experimental sequences validate that the method is more suitable for the dim targets detection and tracking compared with the scheme choosing the higher intensity pixel in tracking. It not only has perfect detection performance but also can greatly enhance tracking performance.
基金Open Fund of the Key Lab of the Ministry of Education for Image Information Processing and Intelligent Control,China(No.200702)
文摘Co-occurrence matrices have been successfully applied in texture classification and segmentation.However,they have poor computation performance in real-time application.In this paper,the efficient co-occurrence matrix solution for defect detection is focused on,and a method of Fuzzy Label Co-occurrence Matrix (FLCM) set is proposed.In this method,all gray levels are supposed to subject to some fuzzy sets called fuzzy tonal sets and three defective features are defined.Features of FLCM set with various parameters are combined for the final judgment.Unlike many methods,image acquired for learning hasn't to be entirely free of defects.It is shown that the method produces high accuracy and can be a competent candidate for plain colour fabric defect detection.
