In the object detection of thermal images,the image degradation phenomena,like the simple texture and the blurred object boundary,result in difficulties in localizing objects and matching the objects with the predefined anchor boxes.Therefore,an object detection algorithm for degraded thermal image based on feature alignment and assisted excitation of key points is proposed.Firstly,the visible image branch is introduced,and the similarity between the thermal domain and the visible domain is improved by calculating the feature difference of specified layers in two branches.Then,feature map concatenation and detection scale are modified to enrich the details of objects in the high-level network layers.Finally,an anchor-free detector with assisted excitation of key points is deployed,and thus the model localizes objects better and learn the instances poorly covered by the predefined anchor boxes.Comparative experiments on two datasets show that the proposed algorithm localizes thermal objects accurately and improves the accuracy of object detection in degraded thermal image effectively.
刘盛, 金坤, 王俊, 叶焕然, 程豪豪. 基于特征对齐和关键点辅助激励的退化热成像图目标检测[J]. 模式识别与人工智能, 2020, 33(12): 1104-1114.
LIU Sheng, JIN Kun, WANG Jun, YE Huanran, CHENG Haohao. Object Detection in Degraded Thermal Image Based on Feature Alignment and Assisted Excitation of Key Points. , 2020, 33(12): 1104-1114.
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2020, Vol. 33 
