Single-pixel imaging(SPI)receives widespread attention due to its superior anti-interference capabilities,and image segmentation technology can effectively facilitate its recognition and information extraction.However...Single-pixel imaging(SPI)receives widespread attention due to its superior anti-interference capabilities,and image segmentation technology can effectively facilitate its recognition and information extraction.However,the complexity of the target scene and plenty of imaging time in SPI make it challenging to achieve high-quality and concise segmentation.In this paper,we investigate the image-free intricate scene semantic segmentation in SPI.Using“learned”illumination patterns allows for the full extraction of the object's spatial information,thereby enabling pixel-level segmentation results through the decoding of the received measurements.Simulation and experimentation show that,in the absence of image reconstruction,the mean intersection over union(MIoU)of segmented image can reach higher than 85%,and the Dice coefficient(DICE)close to 90%even at the sampling ratio of 5%.Our approach may be favorable to applications in medical image segmentation and autonomous driving field.展开更多
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
同步定位与建图(simultaneous localization and mapping, SLAM)技术是移动机器人研究及应用的关键问题,旨在解决机器人在复杂环境中实现自主定位与地图构建等功能。对SLAM的系统组成、关键技术及应用进行了简要介绍;重点围绕特征点法...同步定位与建图(simultaneous localization and mapping, SLAM)技术是移动机器人研究及应用的关键问题,旨在解决机器人在复杂环境中实现自主定位与地图构建等功能。对SLAM的系统组成、关键技术及应用进行了简要介绍;重点围绕特征点法、滤波法、图优化法、多传感器融合和动态场景5个方面,综述了SLAM系统的关键技术、国内外研究现状及标志性应用进展;并结合代表性系统,比较分析了不同方法之间的优缺点,详细阐述了多传感器融合SLAM系统,同时对复杂场景下的SLAM技术进行了展望。展开更多
Due to the existing limited dynamic range a camera cannot reveal all the details in a high-dynamic range scene. In order to solve this problem,this paper presents a multi-exposure fusion method for getting high qualit...Due to the existing limited dynamic range a camera cannot reveal all the details in a high-dynamic range scene. In order to solve this problem,this paper presents a multi-exposure fusion method for getting high quality images in high dynamic range scene. First,a set of multi-exposure images is obtained by multiple exposures in a same scene and their brightness condition is analyzed. Then,multi-exposure images under the same scene are decomposed using dual-tree complex wavelet transform( DT-CWT),and their low and high frequency components are obtained. Weight maps according to the brightness condition are assigned to the low components for fusion. Maximizing the region Sum Modified-Laplacian( SML) is adopted for high-frequency components fusing. Finally,the fused image is acquired by subjecting the low and high frequency coefficients to inverse DT-CWT.Experimental results show that the proposed approach generates high quality results with uniform distributed brightness and rich details. The proposed method is efficient and robust in varies 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
基金Project supported by the Fundamental Research Funds for the Central Universities of China(Grant No.531118010757)。
文摘Single-pixel imaging(SPI)receives widespread attention due to its superior anti-interference capabilities,and image segmentation technology can effectively facilitate its recognition and information extraction.However,the complexity of the target scene and plenty of imaging time in SPI make it challenging to achieve high-quality and concise segmentation.In this paper,we investigate the image-free intricate scene semantic segmentation in SPI.Using“learned”illumination patterns allows for the full extraction of the object's spatial information,thereby enabling pixel-level segmentation results through the decoding of the received measurements.Simulation and experimentation show that,in the absence of image reconstruction,the mean intersection over union(MIoU)of segmented image can reach higher than 85%,and the Dice coefficient(DICE)close to 90%even at the sampling ratio of 5%.Our approach may be favorable to applications in medical image segmentation and autonomous driving field.
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.
文摘同步定位与建图(simultaneous localization and mapping, SLAM)技术是移动机器人研究及应用的关键问题,旨在解决机器人在复杂环境中实现自主定位与地图构建等功能。对SLAM的系统组成、关键技术及应用进行了简要介绍;重点围绕特征点法、滤波法、图优化法、多传感器融合和动态场景5个方面,综述了SLAM系统的关键技术、国内外研究现状及标志性应用进展;并结合代表性系统,比较分析了不同方法之间的优缺点,详细阐述了多传感器融合SLAM系统,同时对复杂场景下的SLAM技术进行了展望。
基金Supported by the National Natural Science Foundation of China(No.61308099,61304032)
文摘Due to the existing limited dynamic range a camera cannot reveal all the details in a high-dynamic range scene. In order to solve this problem,this paper presents a multi-exposure fusion method for getting high quality images in high dynamic range scene. First,a set of multi-exposure images is obtained by multiple exposures in a same scene and their brightness condition is analyzed. Then,multi-exposure images under the same scene are decomposed using dual-tree complex wavelet transform( DT-CWT),and their low and high frequency components are obtained. Weight maps according to the brightness condition are assigned to the low components for fusion. Maximizing the region Sum Modified-Laplacian( SML) is adopted for high-frequency components fusing. Finally,the fused image is acquired by subjecting the low and high frequency coefficients to inverse DT-CWT.Experimental results show that the proposed approach generates high quality results with uniform distributed brightness and rich details. The proposed method is efficient and robust in varies 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.
