Towards complex scenes:A deep learning-based camouflaged people detection method for snapshot multispectral images

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外文摘要：Camouflaged people are extremely expert in actively concealing themselves by effectively utilizing cover and the surrounding environment.Despite advancements in optical detection capabilities through im-aging 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 SimAM 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 infor-mation 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.

外文关键词：

Camouflaged people detectionSnapshot multispectral imagingOptimal band selectionMS-YOLOComplex remote sensing scenes

作者：

Shu Wang、Dawei Zeng、Yixuan Xu、Gonghan Yang、Feng Huang、Liqiong Chen

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作者单位：

School of Mechanical Engineering and Automation,Fuzhou University,Fuzhou 350108,China

基金：

国家自然科学基金福建省自然科学基金福建省自然科学基金Education and Scientific Research Program for Young and Middleaged Teachers in Fujian Province

项目编号：

620050492020J014512022J05113JAT210035

出版年：

2024

DOI：

10.1016/j.dt.2023.12.011

防务技术

中国兵工学会

防务技术

CSTPCD

影响因子：0.358

ISSN：2214-9147

年,卷(期)：2024.34(4)