A Strategy of Subsea Pipeline Identification with Sidescan Sonar based on YOLOV5 Model
文献类型：会议论文
| 作者 | Li Y(李岩)2,3 ; Wu MY(武美妍)2,3,4; Guo JH(郭家宏)1,2,3; Huang Y(黄琰)2,3
|
| 出版日期 | 2021 |
| 会议日期 | October 12-15, 2021 |
| 会议地点 | Jeju, Korea, Republic of |
| 关键词 | Subsea Pipeline Identification Deep Learning Faster RCNN Model Sidescan Sonar Acoustic Image |
| 页码 | 500-505 |
| 英文摘要 | Accurate identification of pipelines is the basis and prerequisite for tracking and inspection of subsea pipelines with the help of autonomous unmanned vehicles. In this paper, we proposed a strategy based on a deep learning model YOLOV5 to extract the subsea pipeline from acoustic images acquired by a Side scan sonar (SSS). Considering the imaging mechanisms of SSS, the formed bar image by SSS in a short certain period is segmented into many sub-images. Subsequently, these sub-images are fed into a pre-trained identification model based on YOLOV5 to extract the subsea pipelines. This strategy ensures the subsea pipeline could be detected with low time consumption and satisfactory accuracy. The average precision (AP) of our proposed subsea pipeline identification strategy achieved 97.62% with 304ms time consumption for the bar image formed in the 10s period. The experimental results demonstrate that the performance of the proposed subsea pipeline identification strategy is superior comparing with other state-of-the-art models in the performance of both identification and real-time. |
| 产权排序 | 1 |
| 会议录 | 2021 21st International Conference on Control, Automation and Systems, ICCAS 2021
 |
| 会议录出版者 | IEEE Computer Society |
| 会议录出版地 | Washington |
| 语种 | 英语 |
| ISSN号 | 1598-7833 |
| ISBN号 | 978-89-93215-21-2 |
| WOS记录号 | WOS:000750950700067 |
| 源URL | [http://ir.sia.cn/handle/173321/30349]  |
| 专题 | 海洋机器人卓越创新中心 |
| 通讯作者 | Huang Y(黄琰) |
| 作者单位 | 1.School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China 2.Institutes of Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 3.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 4.School of Artificial Intelligence, Shenyang University of Technology, Shenyang 110870, China |
| 推荐引用方式 GB/T 7714 | Li Y,Wu MY,Guo JH,et al. A Strategy of Subsea Pipeline Identification with Sidescan Sonar based on YOLOV5 Model[C]. 见:. Jeju, Korea, Republic of. October 12-15, 2021. |
入库方式： OAI收割
来源：沈阳自动化研究所
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