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Automatic detection and extraction of lost shipping containers based on YOLO and the segment anything model

文献类型：期刊论文

作者Li, Hongtao3,4; Yang, Yong3,4; Wang, Shengping2; Chen, Zhigao2; He, Linbang1
刊名REMOTE SENSING LETTERS
出版日期2024-10-02
卷号15期号:10页码:1023-1034
关键词Multi-beam sonar water column image lost shipping container zero-shot transfer image segmentation target extraction
ISSN号2150-704X
DOI10.1080/2150704X.2024.2398814
英文摘要The artificial visual method is currently commonly used to decipher multi-beam water column images to obtain the position and state of lost shipping containers. However, the recognition efficiency and accuracy of this method need to be improved. The You Only Look Once (YOLO) series model has strong real-time target detection capability. Meanwhile, the Segment Anything Model (SAM) has strong zero-shot transferability. A detection and extraction method for lost shipping containers, which combines the two models mentioned above, is proposed in this study. First, the YOLO series model is employed to detect lost shipping container targets in a single-frame water column image. On this basis, the output of bounding box positions by the optimal target detection model is used as a prompt for the SAM. Finally, the SAM is used to extract lost shipping container targets in images through zero-shot transferability. Experimental results in the Pearl River estuary show that the combined modelling method of YOLOv5-n and EdgeSAM-3x achieves the best overall performance. The precision and recall for the detection of lost shipping containers by this method is better than 95%. In terms of target extraction, YOLOv5-n and EdgeSAM-3x have the best Intersection over Union, recall, and F1 scores.
WOS关键词WATER COLUMN DATA
资助项目Hainan Province Science and Technology Special Fund[ZDYF2024SHFZ086] ; Key Research and Development Program of Jiangxi Province[20212BBE53031] ; National Natural Science Foundation of China[20232BAB204089] ; Natural Science Foundation of Jiangxi Province[20232BAB204089]
WOS研究方向Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001309265100001
出版者TAYLOR & FRANCIS LTD
资助机构Hainan Province Science and Technology Special Fund ; Key Research and Development Program of Jiangxi Province ; National Natural Science Foundation of China ; Natural Science Foundation of Jiangxi Province
源URL[http://ir.idsse.ac.cn/handle/183446/11389]  
专题深海工程技术部_网信/深海软件测评及研发研究室
通讯作者Li, Hongtao
作者单位1.Chinese Acad Sci, Inst Deep sea Sci & Engn, Sanya, Peoples R China
2.East China Univ Technol, Sch Surveying & Geoinformat Engn, Nanchang, Peoples R China
3.Harbin Engn Univ, Nanhai Inst, Sanya, Peoples R China
4.Harbin Engn Univ, Coll Underwater Acoust Engn, 145 Nantong St, Harbin 150001, Peoples R China
推荐引用方式
GB/T 7714		 Li, Hongtao,Yang, Yong,Wang, Shengping,et al. Automatic detection and extraction of lost shipping containers based on YOLO and the segment anything model[J]. REMOTE SENSING LETTERS,2024,15(10):1023-1034.
APA Li, Hongtao,Yang, Yong,Wang, Shengping,Chen, Zhigao,&He, Linbang.(2024).Automatic detection and extraction of lost shipping containers based on YOLO and the segment anything model.REMOTE SENSING LETTERS,15(10),1023-1034.
MLA Li, Hongtao,et al."Automatic detection and extraction of lost shipping containers based on YOLO and the segment anything model".REMOTE SENSING LETTERS 15.10(2024):1023-1034.

入库方式： OAI收割

来源：深海科学与工程研究所

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