Underwater Object Detection based on YOLO-v3 network
文献类型：会议论文
| 作者 | Wang YM(王艳美)3,4,5; Liu JX(刘佳鑫)1; Yu SQ(余思泉)3,5 ; Wang, Kai2; Han Z(韩志)3,5; Tang YD(唐延东)3,5
|
| 出版日期 | 2021 |
| 会议日期 | October 15-17, 2021 |
| 会议地点 | Beijing, China |
| 关键词 | underwater object detection YOLO-v3 network side scan sonar image underwater object dataset |
| 页码 | 571-575 |
| 英文摘要 | Recently, side scan sonar (SSS) is increasingly applied to underwater search, which can display the microgeomorphic morphology and distribution, and obtain a continuous two-dimensional submarine acoustic map with a certain width. Automatic underwater object detection methods can help a lot in case of long searches, where sonar operators may feel exhausted and therefore miss the possible object. This paper proposes an underwater object detection method based on YOLO-v3 network. We first establish a real side scan sonar image data-set, which includes 7000 sonar images with four types of objects. Secondly, we propose an underwater object detection system based on side scan sonar images and YOLO-v3 network. Finally, we carried out extensive experiments in the real underwater environment to prove the effectiveness of our algorithm. Our work indicates that the YOLO-v3 network is an effective way to improve the accuracy of underwater object detection. |
| 源文献作者 | Beijing Institute of Technology ; Chinese Institute of Command and Control (CICC) ; IEEE Beijing Section ; Tongji University |
| 产权排序 | 1 |
| 会议录 | Proceedings of 2021 IEEE International Conference on Unmanned Systems, ICUS 2021
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-0-7381-4657-7 |
| 源URL | [http://ir.sia.cn/handle/173321/30352]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Wang YM(王艳美) |
| 作者单位 | 1.State Grid Liaoning Electric Power Research Institute, Shenyang, China 2.State Grid Shandong Electric Power Company, Jinan, China 3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences. Shenyang, China 4.University of Chinese Academy of Sciences. Shenyang, China 5.State Key Laboratory of Robotics Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China |
| 推荐引用方式 GB/T 7714 | Wang YM,Liu JX,Yu SQ,et al. Underwater Object Detection based on YOLO-v3 network[C]. 见:. Beijing, China. October 15-17, 2021. |
入库方式： OAI收割
来源：沈阳自动化研究所
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