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Underwater target detection and embedded deployment based on lightweight YOLO_GN

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Abstract

In order to solve the problem of missing various targets due to the limited memory and computing power of underwater equipment and also the complexity of the underwater environment, a lightweight and efficient underwater target detection algorithm YOLO_GN (YOLO with Ghost network) is proposed. Based on the basic framework of YOLOv5s, the algorithm designs a new backbone using GhostNetV2 and proposes Ghost_BottleneckV2 combined with dynamic sparse attention BiFormer to reduce computational costs and improve detection accuracy. The lightweight multi-scale convolutional LW-GSConv combined with VOV-GSCSP is used to capture the complex features of the input data more accurately and improve the network expression ability. In view of the imbalance of a large number of detection samples in underwater targets, the SlideLoss function is introduced and the optimizer of original model is updated to Sophia, so that the algorithm model has better generalization ability. Finally, the YOLO_GN algorithm is equipped with the Raspberry Pi 4B development board, and the camera is called up to realize real-time detection of underwater targets. Simulation results show that the proposed method can achieve 85.35% detection accuracy on the URPC dataset, which is 2.43% higher than the most common architecture in underwater scenarios, and computational complexity is reduced by 46.47%. Moreover, it can achieve better object detection effect in embedded terminals.

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Data availability

Datasets (URPC2020) used in this work are publicly available in the Internet.

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Acknowledgments

This work was supported by the Key project of Natural Science Foundation of Shaanxi Province, Grant. 2024JC-YBQN-0724 and National Natural Science Foundation of China, 62031021, Grant Number 62031021.

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Authors and Affiliations

  1. Shaanxi University of Science and Technology, Xi’an City, 710021, Shaanxi Province, China

    Xiao Chen, Chenye Fan, Jingjing Shi, Haiyan Wang & Haiyang Yao

  2. Northwestern Polytechnical University, Xi’an City, 710072, Shaanxi Province, China

    Haiyan Wang

Authors
  1. Xiao Chen

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  2. Chenye Fan

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Contributions

Conceptualization, Xiao Chen and Chenye Fan; Formal analysis, Jingjing Shi, Xiao Chen and Chenye Fan; Funding acquisition, Haiyan Wang; Investigation, Haiyang Yao; Methodology, Xiao Chen and Chenye Fan; Resources, Xiao Chen and Chenye Fan; Software, Chenye Fan; Validation, Chenye Fan; Writing – original draft, Xiao Chen and Chenye Fan; Writing – review & editing, Jingjing Shi, Xiao Chen, Chenye Fan, Haiyang Yao and Haiyan Wang. All authors have read and agreed to the published version of the manuscript.

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Correspondence toHaiyan Wang.

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Chen, X., Fan, C., Shi, J.et al. Underwater target detection and embedded deployment based on lightweight YOLO_GN.J Supercomput80, 14057–14084 (2024). https://doi.org/10.1007/s11227-024-06020-0

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