An Underwater Object Detection Method for Sonar Image Based on YOLOv3 Model

WANG Fei; WANG Xinyu; ZHOU Jingchun; LIU Miao

doi:10.11999/JEIT220260

Volume 44 Issue 10

Oct. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(10): 3419-3426

WANG Fei, WANG Xinyu, ZHOU Jingchun, LIU Miao. An Underwater Object Detection Method for Sonar Image Based on YOLOv3 Model[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3419-3426. doi: 10.11999/JEIT220260

Citation:

WANG Fei, WANG Xinyu, ZHOU Jingchun, LIU Miao. An Underwater Object Detection Method for Sonar Image Based on YOLOv3 Model[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3419-3426. doi: 10.11999/JEIT220260

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An Underwater Object Detection Method for Sonar Image Based on YOLOv3 Model

doi: 10.11999/JEIT220260

Information Science and Technology College, Dalian Maritime University, Dalian 116026, China

Funds: The National Natural Science Foundation of China(62103072), China Postdoctoral Science Foundation (2021M690502), The Fundamental Research Funds for the Central Universities (3132022248)

Received Date: 2022-03-10
Rev Recd Date: 2022-05-23

Available Online: 2022-06-15

Publish Date: 2022-10-19

Abstract

Abstract

Applying the object detection framework to the processing of underwater sonar images is a recent high-profile topic. Existing detection methods for sonar data are mainly based on the texture of sonar image. These methods are not able to handle the unstable geometric shape of objects in sonar image. To this end, a YOLOv3-based underwater object detection model YOLOv3F is proposed, which fuses texture features extracted from sonar images with spatial geometric features extracted from point clouds, and then the fused features are used for target detection. The experimental results show that the performance of the proposed improved model is significantly improved compared with the three control models in the experimental setup when different intersection of detection frames are set and compared to the detection of targets of different scales; The improved model also shows better detection results compared with YOLOv3 in the case of detection of a single class of objects.
- Underwater object detection,
- Sonar image,
- Depth image,
- YOLOv3 model

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References(18)

References

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