基于EfficientDet的无人水面车辆舰船目标检测

IF 3.2 Q2 AUTOMATION & CONTROL SYSTEMS
Ronghui Li, Jinshan Wu, Liang Cao
{"title":"基于EfficientDet的无人水面车辆舰船目标检测","authors":"Ronghui Li, Jinshan Wu, Liang Cao","doi":"10.1080/21642583.2021.1990159","DOIUrl":null,"url":null,"abstract":"The autonomous navigation of unmanned surface vehicles (USV) depends mainly on effective ship target detection to the nearby water area. The difficulty of target detection for USV derives from the complexity of the external environment, such as the light reflection and the cloud or mist shield. Accordingly, this paper proposes a target detection technology for USV on the basis of the EfficientDet algorithm. The ship features fusion is performed by Bi-directional Feature Pyra-mid Network (BiFPN), in which the pre-trained EfficientNet via ImageNet is taken as the backbone network, then the detection speed is increased by group normalization. Compared with the Faster-RCNN and Yolo V3, the ship target detection accuracy is greatly improved to 87.5% in complex environments. The algorithm can be applied to the identification of dynamic targets on the sea, which provides a key reference for the autonomous navigation of USV and the military threats assessment on the sea surface.","PeriodicalId":46282,"journal":{"name":"Systems Science & Control Engineering","volume":"10 1","pages":"264 - 271"},"PeriodicalIF":3.2000,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Ship target detection of unmanned surface vehicle base on EfficientDet\",\"authors\":\"Ronghui Li, Jinshan Wu, Liang Cao\",\"doi\":\"10.1080/21642583.2021.1990159\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The autonomous navigation of unmanned surface vehicles (USV) depends mainly on effective ship target detection to the nearby water area. The difficulty of target detection for USV derives from the complexity of the external environment, such as the light reflection and the cloud or mist shield. Accordingly, this paper proposes a target detection technology for USV on the basis of the EfficientDet algorithm. The ship features fusion is performed by Bi-directional Feature Pyra-mid Network (BiFPN), in which the pre-trained EfficientNet via ImageNet is taken as the backbone network, then the detection speed is increased by group normalization. Compared with the Faster-RCNN and Yolo V3, the ship target detection accuracy is greatly improved to 87.5% in complex environments. The algorithm can be applied to the identification of dynamic targets on the sea, which provides a key reference for the autonomous navigation of USV and the military threats assessment on the sea surface.\",\"PeriodicalId\":46282,\"journal\":{\"name\":\"Systems Science & Control Engineering\",\"volume\":\"10 1\",\"pages\":\"264 - 271\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2021-10-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Systems Science & Control Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/21642583.2021.1990159\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems Science & Control Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/21642583.2021.1990159","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 6

摘要

无人水面车辆的自主导航主要依赖于对附近水域的有效船舶目标检测。USV目标检测的困难源于外部环境的复杂性,如光反射和云层或薄雾遮挡。因此,本文提出了一种基于EfficientDet算法的无人潜航目标检测技术。船舶特征融合采用双向特征Pyra中间网络(BiFPN)进行,其中以通过ImageNet预先训练的EfficientNet作为骨干网络,然后通过组归一化来提高检测速度。与Faster RCNN和Yolo V3相比,在复杂环境中,船舶目标检测精度大大提高到87.5%。该算法可应用于海面动态目标识别,为USV自主导航和海面军事威胁评估提供了重要参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ship target detection of unmanned surface vehicle base on EfficientDet
The autonomous navigation of unmanned surface vehicles (USV) depends mainly on effective ship target detection to the nearby water area. The difficulty of target detection for USV derives from the complexity of the external environment, such as the light reflection and the cloud or mist shield. Accordingly, this paper proposes a target detection technology for USV on the basis of the EfficientDet algorithm. The ship features fusion is performed by Bi-directional Feature Pyra-mid Network (BiFPN), in which the pre-trained EfficientNet via ImageNet is taken as the backbone network, then the detection speed is increased by group normalization. Compared with the Faster-RCNN and Yolo V3, the ship target detection accuracy is greatly improved to 87.5% in complex environments. The algorithm can be applied to the identification of dynamic targets on the sea, which provides a key reference for the autonomous navigation of USV and the military threats assessment on the sea surface.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Systems Science & Control Engineering
Systems Science & Control Engineering AUTOMATION & CONTROL SYSTEMS-
CiteScore
9.50
自引率
2.40%
发文量
70
审稿时长
29 weeks
期刊介绍: Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory
×
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