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
In the field of automatic target recognition and tracking,traditional image complexity metrics,such as statistical variance and signal-to-noise ratio,all focus on single-frame images.However,there are few researches a...In the field of automatic target recognition and tracking,traditional image complexity metrics,such as statistical variance and signal-to-noise ratio,all focus on single-frame images.However,there are few researches about the complexity of image sequence.To solve this problem,a criterion of evaluating image sequence complexity is proposed.Firstly,to characterize this criterion quantitatively,two metrics for measuring the complexity of image sequence,namely feature space similarity degree of global background(FSSDGB)and feature space occultation degree of local background(FSODLB)are developed.Here,FSSDGB reflects the ability of global background to introduce false alarms based on feature space,and FSODLB represents the difference between target and local background based on feature space.Secondly,the feature space is optimized by the grey relational method and relevant features are removed so that FSSDGB and FSODLB are more reasonable to establish complexity of single-frame images.Finally,the image sequence complexity is not a linear sum of the single-frame image complexity.Target tracking errors often occur in high-complexity images and the tracking effect of low-complexity images is very well.The nonlinear transformation based on median(NTM)is proposed to construct complexity of image sequence.The experimental results show that the proposed metric is more valid than other metrics,such as sequence correlation(SC)and interframe change degree(IFCD),and it is highly relevant to the actual performance of automatic target tracking algorithms.展开更多
针对雨雾等复杂天气下无人机图像质量下降导致目标检测效果不佳的问题,提出基于上下文引导和提示学习的目标检测算法CGP-YOLO(context-guided and prompt-based YOLOv8)。构建一个多任务联合学习的检测网络,通过双分支结构达到平衡图像...针对雨雾等复杂天气下无人机图像质量下降导致目标检测效果不佳的问题,提出基于上下文引导和提示学习的目标检测算法CGP-YOLO(context-guided and prompt-based YOLOv8)。构建一个多任务联合学习的检测网络,通过双分支结构达到平衡图像检测和恢复的任务。提出基于提示学习的跨层注意力加权图像去噪分支,指导网络利用退化提示重构清晰的图像;模型主干设计基于上下文的残差采样模块,集成卷积注意力机制,综合目标的局部和全局信息;采用可分离大核多尺度特征提取模块,处理网络多尺度特征;引入小目标的专用检测头,增强小目标的检测精度。实验结果表明,在参数量仅为基线模型60%的情况下,该模型的检测精度提高了2.4个百分点,平均精度(mAP)提高了2.04个百分点,模型检测效果优于其他经典模型,具备卓越的性能。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supported by the National Natural Science Foundation of China(61703337)Shanghai Aerospace Science and Technology Innovation Fund(SAST2017-082)
文摘In the field of automatic target recognition and tracking,traditional image complexity metrics,such as statistical variance and signal-to-noise ratio,all focus on single-frame images.However,there are few researches about the complexity of image sequence.To solve this problem,a criterion of evaluating image sequence complexity is proposed.Firstly,to characterize this criterion quantitatively,two metrics for measuring the complexity of image sequence,namely feature space similarity degree of global background(FSSDGB)and feature space occultation degree of local background(FSODLB)are developed.Here,FSSDGB reflects the ability of global background to introduce false alarms based on feature space,and FSODLB represents the difference between target and local background based on feature space.Secondly,the feature space is optimized by the grey relational method and relevant features are removed so that FSSDGB and FSODLB are more reasonable to establish complexity of single-frame images.Finally,the image sequence complexity is not a linear sum of the single-frame image complexity.Target tracking errors often occur in high-complexity images and the tracking effect of low-complexity images is very well.The nonlinear transformation based on median(NTM)is proposed to construct complexity of image sequence.The experimental results show that the proposed metric is more valid than other metrics,such as sequence correlation(SC)and interframe change degree(IFCD),and it is highly relevant to the actual performance of automatic target tracking algorithms.
文摘针对雨雾等复杂天气下无人机图像质量下降导致目标检测效果不佳的问题,提出基于上下文引导和提示学习的目标检测算法CGP-YOLO(context-guided and prompt-based YOLOv8)。构建一个多任务联合学习的检测网络,通过双分支结构达到平衡图像检测和恢复的任务。提出基于提示学习的跨层注意力加权图像去噪分支,指导网络利用退化提示重构清晰的图像;模型主干设计基于上下文的残差采样模块,集成卷积注意力机制,综合目标的局部和全局信息;采用可分离大核多尺度特征提取模块,处理网络多尺度特征;引入小目标的专用检测头,增强小目标的检测精度。实验结果表明,在参数量仅为基线模型60%的情况下,该模型的检测精度提高了2.4个百分点,平均精度(mAP)提高了2.04个百分点,模型检测效果优于其他经典模型,具备卓越的性能。
文摘目的探讨MR磁敏感加权成像(susceptibility-weighted imaging,SWI)在鉴别BosniakⅡF~Ⅲ级肾脏囊性病变(cystic renal masses,CRMs)良恶性中的应用价值。材料与方法回顾性分析38例BosniakⅡF~Ⅲ级CRMs患者的影像特征,以病理诊断作为金标准分为良性组(17例)、恶性组(21例)。观察两组病变的T1WI、T2WI及SWI图像,记录病变大小、形状、T1WI与T2WI图像上病变囊腔信号及瘤内磁敏感信号(intratumoral susceptibility signal intensity,ITSS)评价情况(包括ITSS主要结构、出血灶数目、微血管数目及实性成分中ITSS所占面积比)。利用卡方检验比较两组病变T1WI、T2WI囊腔信号差异;以Mann-Whitney U检验比较两组病变ITSS显示情况的差异;以Kappa检验比较两位观察者评级一致性;利用二元logistic回归分析构建ITSS评价指标联合预测因子;受试者工作特征(receiver operating characteristic,ROC)曲线分析不同ITSS评价指标及联合预测因子鉴别良恶性病变的诊断效能,DeLong检验比较曲线下面积(area under the curve,AUC)差异。结果BosniakⅡF~Ⅲ级肾脏囊性病变在T1WI、T2WI上的信号在良恶性组中的差异无统计学意义(P>0.05)。良性组的ITSS主要结构评级高于恶性组,但二者差异无统计学意义(P>0.05)。恶性组中出血灶数目、微血管数目及实性成分中ITSS所占面积比评级均高于良性组,且二者差异具有统计学意义(P<0.01)。两位观察者对良恶性组病变ITSS主要结构及实性成分中ITSS所占面积比评价结果一致性较好(Kappa值为0.72和0.74),对出血灶数目、微血管数目的评价结果很好(Kappa值为0.90和0.84)。出血灶数目、微血管数目、实性成分中ITSS所占面积比及联合预测因子鉴别BosniakⅡF~Ⅲ级CRMs良恶性的AUC分别为0.695[95%置信区间(confidenceinterval,CI):0.520~0.869]、0.868(95%CI:0.757~0.980)、0.877(95%CI:0.771~0.983)和0.943(95%CI:0.877~1.000)。DeLong检验结果显示,联合预测因子的诊断效能优于单一ITSS评价指标,差异有统计学意义(P=0.02、P<0.01、P<0.01)结论通过对SWI图像上磁敏感信号的分析,能够为鉴别BosniakⅡF~Ⅲ级CRMs的良恶性提供有价值的信息,为临床诊疗提供可靠的影像依据。
