Military camouflaged object detection with deep learning using dataset development and combination

Kyo-Seong Hwang, Jungmok Ma
{"title":"Military camouflaged object detection with deep learning using dataset development and combination","authors":"Kyo-Seong Hwang, Jungmok Ma","doi":"10.1177/15485129241233299","DOIUrl":null,"url":null,"abstract":"Camouflaged object detection (COD) is one of the emerging artificial intelligence technologies. COD identifies objects that require attention and time to detect with human eyes due to the similarity in texture or color to the surrounding environment. Despite the importance of camouflage and its detection in military, there is a lack of military camouflaged object detection research. Previous studies point out that the general COD has not been well studied due to the lack of camouflaged datasets, and the situation is worse in the military domain. This study aims at tackling the challenge in two directions. First, we carefully assemble the military camouflaged object (MCAM) dataset, including camouflaged soldiers and people as well as camouflaged military supplies for military COD. The experiment shows that MCAM can generate better performance results than the other benchmark datasets (CAMO, COD10K). Second, military (MCAM) and nonmilitary camouflage datasets (benchmark datasets) are combined and tested to overcome data scarcity. The experiment shows that the nonmilitary camouflage datasets are effective for military COD at a certain level, and a proper combination of military and nonmilitary camouflage datasets can improve the detection performance.","PeriodicalId":508000,"journal":{"name":"The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/15485129241233299","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Camouflaged object detection (COD) is one of the emerging artificial intelligence technologies. COD identifies objects that require attention and time to detect with human eyes due to the similarity in texture or color to the surrounding environment. Despite the importance of camouflage and its detection in military, there is a lack of military camouflaged object detection research. Previous studies point out that the general COD has not been well studied due to the lack of camouflaged datasets, and the situation is worse in the military domain. This study aims at tackling the challenge in two directions. First, we carefully assemble the military camouflaged object (MCAM) dataset, including camouflaged soldiers and people as well as camouflaged military supplies for military COD. The experiment shows that MCAM can generate better performance results than the other benchmark datasets (CAMO, COD10K). Second, military (MCAM) and nonmilitary camouflage datasets (benchmark datasets) are combined and tested to overcome data scarcity. The experiment shows that the nonmilitary camouflage datasets are effective for military COD at a certain level, and a proper combination of military and nonmilitary camouflage datasets can improve the detection performance.
利用数据集开发和组合的深度学习进行军事伪装物体检测
伪装物体检测(COD)是新兴的人工智能技术之一。伪装物体检测是一种新兴的人工智能技术,它能识别由于与周围环境的纹理或颜色相似而需要人眼花费时间和注意力才能探测到的物体。尽管伪装及其检测在军事领域非常重要,但目前缺乏对军事伪装物体检测的研究。以往的研究指出,由于伪装数据集的缺乏,一般的伪装物体检测还没有得到很好的研究,而在军事领域情况更糟。本研究旨在从两个方向应对这一挑战。首先,我们精心组建了军事伪装对象(MCAM)数据集,其中包括伪装士兵、伪装人员以及伪装军用物资,用于军事 COD。实验表明,与其他基准数据集(CAMO、COD10K)相比,MCAM 能产生更好的性能结果。其次,为克服数据稀缺问题,将军事伪装数据集(MCAM)和非军事伪装数据集(基准数据集)结合起来进行测试。实验结果表明,非军用伪装数据集在一定程度上对军用 COD 有效,而军用和非军用伪装数据集的适当组合可以提高检测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信