Research on detection and tracking methods of unmanned ship water targets based on light vision
This study explores technical methods based on light vision to address the problem of target detection and tracking by surface unmanned ships in complex environments. We utilize an improved dark channel dehazing method and guided filtering for image preprocessing to improve the accuracy and efficien...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Command Control and Simulation
2024-12-01
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| Series: | Zhihui kongzhi yu fangzhen |
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| Online Access: | https://www.zhkzyfz.cn/fileup/1673-3819/PDF/1732684222418-1590951157.pdf |
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| _version_ | 1850224849087102976 |
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| author | LIU Yibo, QIU Xinyu, WANG Tianhao, GAOYAN Xiusong, WANG Yintao |
| author_facet | LIU Yibo, QIU Xinyu, WANG Tianhao, GAOYAN Xiusong, WANG Yintao |
| author_sort | LIU Yibo, QIU Xinyu, WANG Tianhao, GAOYAN Xiusong, WANG Yintao |
| collection | DOAJ |
| description | This study explores technical methods based on light vision to address the problem of target detection and tracking by surface unmanned ships in complex environments. We utilize an improved dark channel dehazing method and guided filtering for image preprocessing to improve the accuracy and efficiency of subsequent image processing. In terms of target detection, the YOLOv7 algorithm is used, which effectively improves the accuracy and recall rate of target detection by optimizing the loss function. In order to achieve accurate multi-target tracking, combined with self-trained model weights and Sort algorithm, continuous tracking of targets and accurate annotation of center point trajectories are successfully implemented. In addition, a binocular camera system is built on an unmanned ship platform for target ranging. Experimental results show that our method can achieve the ranging function with an average relative error of 6.46%. This result not only improves the navigation and positioning capabilities of unmanned ships, but also provides technical support for water surface safety monitoring. This research demonstrates that in the field of surface unmanned ships, target detection and tracking problems can be effectively solved by integrating advanced image processing technology and machine learning algorithms. |
| format | Article |
| id | doaj-art-5828722f2183445dbf0dcbcf540e6f4a |
| institution | OA Journals |
| issn | 1673-3819 |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Editorial Office of Command Control and Simulation |
| record_format | Article |
| series | Zhihui kongzhi yu fangzhen |
| spelling | doaj-art-5828722f2183445dbf0dcbcf540e6f4a2025-08-20T02:05:31ZzhoEditorial Office of Command Control and SimulationZhihui kongzhi yu fangzhen1673-38192024-12-01466788610.3969/j.issn.1673-3819.2024.06.013Research on detection and tracking methods of unmanned ship water targets based on light visionLIU Yibo, QIU Xinyu, WANG Tianhao, GAOYAN Xiusong, WANG Yintao01 System Engineering Research Institute, China Shipbuilding Corporation Limited, Beijing 100036, China;2 School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, ChinaThis study explores technical methods based on light vision to address the problem of target detection and tracking by surface unmanned ships in complex environments. We utilize an improved dark channel dehazing method and guided filtering for image preprocessing to improve the accuracy and efficiency of subsequent image processing. In terms of target detection, the YOLOv7 algorithm is used, which effectively improves the accuracy and recall rate of target detection by optimizing the loss function. In order to achieve accurate multi-target tracking, combined with self-trained model weights and Sort algorithm, continuous tracking of targets and accurate annotation of center point trajectories are successfully implemented. In addition, a binocular camera system is built on an unmanned ship platform for target ranging. Experimental results show that our method can achieve the ranging function with an average relative error of 6.46%. This result not only improves the navigation and positioning capabilities of unmanned ships, but also provides technical support for water surface safety monitoring. This research demonstrates that in the field of surface unmanned ships, target detection and tracking problems can be effectively solved by integrating advanced image processing technology and machine learning algorithms.https://www.zhkzyfz.cn/fileup/1673-3819/PDF/1732684222418-1590951157.pdftarget detection|multi-target tracking|defogging |
| spellingShingle | LIU Yibo, QIU Xinyu, WANG Tianhao, GAOYAN Xiusong, WANG Yintao Research on detection and tracking methods of unmanned ship water targets based on light vision Zhihui kongzhi yu fangzhen target detection|multi-target tracking|defogging |
| title | Research on detection and tracking methods of unmanned ship water targets based on light vision |
| title_full | Research on detection and tracking methods of unmanned ship water targets based on light vision |
| title_fullStr | Research on detection and tracking methods of unmanned ship water targets based on light vision |
| title_full_unstemmed | Research on detection and tracking methods of unmanned ship water targets based on light vision |
| title_short | Research on detection and tracking methods of unmanned ship water targets based on light vision |
| title_sort | research on detection and tracking methods of unmanned ship water targets based on light vision |
| topic | target detection|multi-target tracking|defogging |
| url | https://www.zhkzyfz.cn/fileup/1673-3819/PDF/1732684222418-1590951157.pdf |
| work_keys_str_mv | AT liuyiboqiuxinyuwangtianhaogaoyanxiusongwangyintao researchondetectionandtrackingmethodsofunmannedshipwatertargetsbasedonlightvision |