Fast-armored target detection based on multi-scale representation and guided anchor
Fast-armored target detection based on multi-scale representation and guided anchor作者机构：Department of Mechanical EngineeringCollege of Field EngineeringArmy Engineering University of PLANanjing210007China
出 版 物：《Defence Technology（防务技术）》 (Defence Technology)
年 卷 期：2020年第16卷第4期
页 面：922-932页
核心收录：
学科分类：11[军事学] 08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 1109[军事学-军事装备学]
基 金：supported by the National Key Research and Development Program of China under grant 2016YFC0802904 National Natural Science Foundation of China under grant61671470 the Postdoctoral Science Foundation Funded Project of China under grant 2017M623423
主 题：red image RPN Fast-armored target detection based on multi-scale representation and guided anchor
摘 要：Focused on the task of fast and accurate armored target detection in ground battlefield,a detection method based on multi-scale representation network(MS-RN) and shape-fixed Guided Anchor(SF-GA)scheme is ***,considering the large-scale variation and camouflage of armored target,a new MS-RN integrating contextual information in battlefield environment is *** MS-RN extracts deep features from templates with different scales and strengthens the detection ability of small *** targets of different sizes are detected on different representation ***,aiming at the accuracy and real-time detection requirements,improved shape-fixed Guided Anchor is used on feature maps of different scales to recommend regions of interests(ROIs).Different from sliding or random anchor,the SF-GA can filter out 80% of the regions while still improving the recall.A special detection dataset for armored target,named Armored Target Dataset(ARTD),is constructed,based on which the comparable experiments with state-of-art detection methods are *** results show that the proposed method achieves outstanding performance in detection accuracy and efficiency,especially when small armored targets are involved.
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