Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems,...Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems, including spectral, polarization, and infrared technologies, there is still a lack of effective real-time method for accurately detecting small-size and high-efficient camouflaged people in complex real-world scenes. Here, this study proposes a snapshot multispectral image-based camouflaged detection model, multispectral YOLO(MS-YOLO), which utilizes the SPD-Conv and Sim AM modules to effectively represent targets and suppress background interference by exploiting the spatial-spectral target information. Besides, the study constructs the first real-shot multispectral camouflaged people dataset(MSCPD), which encompasses diverse scenes, target scales, and attitudes. To minimize information redundancy, MS-YOLO selects an optimal subset of 12 bands with strong feature representation and minimal inter-band correlation as input. Through experiments on the MSCPD, MS-YOLO achieves a mean Average Precision of 94.31% and real-time detection at 65 frames per second, which confirms the effectiveness and efficiency of our method in detecting camouflaged people in various typical desert and forest scenes. Our approach offers valuable support to improve the perception capabilities of unmanned aerial vehicles in detecting enemy forces and rescuing personnel in battlefield.展开更多
Passive optical motion capture technology is an effective mean to conduct high-precision pose estimation of small scenes of mobile robots;nevertheless,in the case of complex background and stray light interference in ...Passive optical motion capture technology is an effective mean to conduct high-precision pose estimation of small scenes of mobile robots;nevertheless,in the case of complex background and stray light interference in the scene,due to the infuence of target adhesion and environmental reflection,this technology cannot estimate the pose accurately.A passive binocular optical motion capture technology under complex illumination based on binocular camera and fixed retroreflective marker balls has been proposed.By fixing multiple hemispherical retrorefective marker balls on a rigid base,it uses binocular camera for depth estimation to obtain the fixed position relationship between the feature points.After performing unsupervised state estimation without manual operation,it overcomes the infuence of refection spots in the background.Meanwhile,contour extraction and ellipse least square fitting are used to extract the marker balls with incomplete shape as the feature points,so as to solve the problem of target adhesion in the scene.A FANUC m10i-a robot moving with 6-DOF is used for verification using the above methods in a complex lighting environment of a welding laboratory.The result shows that the average of absolute position errors is 5.793mm,the average of absolute rotation errors is 1.997°the average of relative position errors is 0.972 mm,and the average of relative rotation errors is 0.002°.Therefore,this technology meets the requirements of high-precision measurement in a complex lighting environment when estimating the 6-DOF-motion mobile robot and has very significant application prospects in complex scenes.展开更多
Infrared images typically exhibit diverse backgrounds,each potentially containing noise and target-like interference elements.In complex backgrounds,infrared small targets are prone to be submerged by background noise...Infrared images typically exhibit diverse backgrounds,each potentially containing noise and target-like interference elements.In complex backgrounds,infrared small targets are prone to be submerged by background noise due to their low pixel proportion and limited available features,leading to detection failure.To address this problem,this paper proposes an Attention Shift-Invariant Cross-Evolutionary Feature Fusion Network(ASCFNet)tailored for the detection of infrared weak and small targets.The network architecture first designs a Multidimensional Lightweight Pixel-level Attention Module(MLPA),which alleviates the issue of small-target feature suppression during deep network propagation by combining channel reshaping,multi-scale parallel subnet architectures,and local cross-channel interactions.Then,a Multidimensional Shift-Invariant Recall Module(MSIR)is designed to ensure the network remains unaffected by minor input perturbations when processing infrared images,through focusing on the model’s shift invariance.Subsequently,a Cross-Evolutionary Feature Fusion structure(CEFF)is designed to allow flexible and efficient integration of multidimensional feature information from different network hierarchies,thereby achieving complementarity and enhancement among features.Experimental results on three public datasets,SIRST,NUDT-SIRST,and IRST640,demonstrate that our proposed network outperforms advanced algorithms in the field.Specifically,on the NUDT-SIRST dataset,the mAP50,mAP50-95,and metrics reached 99.26%,85.22%,and 99.31%,respectively.Visual evaluations of detection results in diverse scenarios indicate that our algorithm exhibits an increased detection rate and reduced false alarm rate.Our method balances accuracy and real-time performance,and achieves efficient and stable detection of infrared weak and small targets.展开更多
In order to rapidly and accurately detect infrared small and dim targets in the infrared image of complex scene collected by virtual prototyping of space-based downward-looking multiband detection,an improved detectio...In order to rapidly and accurately detect infrared small and dim targets in the infrared image of complex scene collected by virtual prototyping of space-based downward-looking multiband detection,an improved detection algorithm of infrared small and dim target is proposed in this paper.Firstly,the original infrared images are changed into a new infrared patch tensor mode through data reconstruction.Then,the infrared small and dim target detection problems are converted to low-rank tensor recovery problems based on tensor nuclear norm in accordance with patch tensor characteristics,and inverse variance weighted entropy is defined for self-adaptive adjustment of sparseness.Finally,the low-rank tensor recovery problem with noise is solved by alternating the direction method to obtain the sparse target image,and the final small target is worked out by a simple partitioning algorithm.The test results in various spacebased downward-looking complex scenes show that such method can restrain complex background well by virtue of rapid arithmetic speed with high detection probability and low false alarm rate.It is a kind of infrared small and dim target detection method with good performance.展开更多
Aiming at the problem that a single correlation filter model is sensitive to complex scenes such as background interference and occlusion,a tracking algorithm based on multi-time-space perception and instance-specific...Aiming at the problem that a single correlation filter model is sensitive to complex scenes such as background interference and occlusion,a tracking algorithm based on multi-time-space perception and instance-specific proposals is proposed to optimize the mathematical model of the correlation filter(CF).Firstly,according to the consistency of the changes between the object frames and the filter frames,the mask matrix is introduced into the objective function of the filter,so as to extract the spatio-temporal information of the object with background awareness.Secondly,the object function of multi-feature fusion is constructed for the object location,which is optimized by the Lagrange method and solved by closed iteration.In the process of filter optimization,the constraints term of time-space perception is designed to enhance the learning ability of the CF to optimize the final track-ing results.Finally,when the tracking results fluctuate,the boundary suppres-sion factor is introduced into the instance-specific proposals to reduce the risk of model drift effectively.The accuracy and success rate of the proposed algorithm are verified by simulation analysis on two popular benchmarks,the object tracking benchmark 2015(OTB2015)and the temple color 128(TC-128).Extensive experimental results illustrate that the optimized appearance model of the proposed algorithm is effective.The distance precision rate and overlap success rate of the proposed algorithm are 0.756 and 0.656 on the OTB2015 benchmark,which are better than the results of other competing algorithms.The results of this study can solve the problem of real-time object tracking in the real traffic environment and provide a specific reference for the detection of traffic abnormalities.展开更多
基金support by the National Natural Science Foundation of China (Grant No. 62005049)Natural Science Foundation of Fujian Province (Grant Nos. 2020J01451, 2022J05113)Education and Scientific Research Program for Young and Middleaged Teachers in Fujian Province (Grant No. JAT210035)。
文摘Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment. Despite advancements in optical detection capabilities through imaging systems, including spectral, polarization, and infrared technologies, there is still a lack of effective real-time method for accurately detecting small-size and high-efficient camouflaged people in complex real-world scenes. Here, this study proposes a snapshot multispectral image-based camouflaged detection model, multispectral YOLO(MS-YOLO), which utilizes the SPD-Conv and Sim AM modules to effectively represent targets and suppress background interference by exploiting the spatial-spectral target information. Besides, the study constructs the first real-shot multispectral camouflaged people dataset(MSCPD), which encompasses diverse scenes, target scales, and attitudes. To minimize information redundancy, MS-YOLO selects an optimal subset of 12 bands with strong feature representation and minimal inter-band correlation as input. Through experiments on the MSCPD, MS-YOLO achieves a mean Average Precision of 94.31% and real-time detection at 65 frames per second, which confirms the effectiveness and efficiency of our method in detecting camouflaged people in various typical desert and forest scenes. Our approach offers valuable support to improve the perception capabilities of unmanned aerial vehicles in detecting enemy forces and rescuing personnel in battlefield.
基金the National Key Research and Development Program of China(No.2018YFB1305005)。
文摘Passive optical motion capture technology is an effective mean to conduct high-precision pose estimation of small scenes of mobile robots;nevertheless,in the case of complex background and stray light interference in the scene,due to the infuence of target adhesion and environmental reflection,this technology cannot estimate the pose accurately.A passive binocular optical motion capture technology under complex illumination based on binocular camera and fixed retroreflective marker balls has been proposed.By fixing multiple hemispherical retrorefective marker balls on a rigid base,it uses binocular camera for depth estimation to obtain the fixed position relationship between the feature points.After performing unsupervised state estimation without manual operation,it overcomes the infuence of refection spots in the background.Meanwhile,contour extraction and ellipse least square fitting are used to extract the marker balls with incomplete shape as the feature points,so as to solve the problem of target adhesion in the scene.A FANUC m10i-a robot moving with 6-DOF is used for verification using the above methods in a complex lighting environment of a welding laboratory.The result shows that the average of absolute position errors is 5.793mm,the average of absolute rotation errors is 1.997°the average of relative position errors is 0.972 mm,and the average of relative rotation errors is 0.002°.Therefore,this technology meets the requirements of high-precision measurement in a complex lighting environment when estimating the 6-DOF-motion mobile robot and has very significant application prospects in complex scenes.
基金supported in part by the National Natural Science Foundation of China under Grant 62271302the Shanghai Municipal Natural Science Foundation under Grant 20ZR1423500.
文摘Infrared images typically exhibit diverse backgrounds,each potentially containing noise and target-like interference elements.In complex backgrounds,infrared small targets are prone to be submerged by background noise due to their low pixel proportion and limited available features,leading to detection failure.To address this problem,this paper proposes an Attention Shift-Invariant Cross-Evolutionary Feature Fusion Network(ASCFNet)tailored for the detection of infrared weak and small targets.The network architecture first designs a Multidimensional Lightweight Pixel-level Attention Module(MLPA),which alleviates the issue of small-target feature suppression during deep network propagation by combining channel reshaping,multi-scale parallel subnet architectures,and local cross-channel interactions.Then,a Multidimensional Shift-Invariant Recall Module(MSIR)is designed to ensure the network remains unaffected by minor input perturbations when processing infrared images,through focusing on the model’s shift invariance.Subsequently,a Cross-Evolutionary Feature Fusion structure(CEFF)is designed to allow flexible and efficient integration of multidimensional feature information from different network hierarchies,thereby achieving complementarity and enhancement among features.Experimental results on three public datasets,SIRST,NUDT-SIRST,and IRST640,demonstrate that our proposed network outperforms advanced algorithms in the field.Specifically,on the NUDT-SIRST dataset,the mAP50,mAP50-95,and metrics reached 99.26%,85.22%,and 99.31%,respectively.Visual evaluations of detection results in diverse scenarios indicate that our algorithm exhibits an increased detection rate and reduced false alarm rate.Our method balances accuracy and real-time performance,and achieves efficient and stable detection of infrared weak and small targets.
文摘In order to rapidly and accurately detect infrared small and dim targets in the infrared image of complex scene collected by virtual prototyping of space-based downward-looking multiband detection,an improved detection algorithm of infrared small and dim target is proposed in this paper.Firstly,the original infrared images are changed into a new infrared patch tensor mode through data reconstruction.Then,the infrared small and dim target detection problems are converted to low-rank tensor recovery problems based on tensor nuclear norm in accordance with patch tensor characteristics,and inverse variance weighted entropy is defined for self-adaptive adjustment of sparseness.Finally,the low-rank tensor recovery problem with noise is solved by alternating the direction method to obtain the sparse target image,and the final small target is worked out by a simple partitioning algorithm.The test results in various spacebased downward-looking complex scenes show that such method can restrain complex background well by virtue of rapid arithmetic speed with high detection probability and low false alarm rate.It is a kind of infrared small and dim target detection method with good performance.
基金funded by the Basic Science Major Foundation(Natural Science)of the Jiangsu Higher Education Institutions of China(Grant:22KJA520012)the Xuzhou Science and Technology Plan Project(Grant:KC21303,KC22305)the sixth“333 project”of Jiangsu Province.
文摘Aiming at the problem that a single correlation filter model is sensitive to complex scenes such as background interference and occlusion,a tracking algorithm based on multi-time-space perception and instance-specific proposals is proposed to optimize the mathematical model of the correlation filter(CF).Firstly,according to the consistency of the changes between the object frames and the filter frames,the mask matrix is introduced into the objective function of the filter,so as to extract the spatio-temporal information of the object with background awareness.Secondly,the object function of multi-feature fusion is constructed for the object location,which is optimized by the Lagrange method and solved by closed iteration.In the process of filter optimization,the constraints term of time-space perception is designed to enhance the learning ability of the CF to optimize the final track-ing results.Finally,when the tracking results fluctuate,the boundary suppres-sion factor is introduced into the instance-specific proposals to reduce the risk of model drift effectively.The accuracy and success rate of the proposed algorithm are verified by simulation analysis on two popular benchmarks,the object tracking benchmark 2015(OTB2015)and the temple color 128(TC-128).Extensive experimental results illustrate that the optimized appearance model of the proposed algorithm is effective.The distance precision rate and overlap success rate of the proposed algorithm are 0.756 and 0.656 on the OTB2015 benchmark,which are better than the results of other competing algorithms.The results of this study can solve the problem of real-time object tracking in the real traffic environment and provide a specific reference for the detection of traffic abnormalities.
