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Real-time underwater target detection based on improved YOLOv7

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Abstract

Underwater target detection is crucial for ocean exploration, but existing methods struggle to achieve satisfactory results due to the complexity of the underwater environment. To enhance the accuracy and real-time performance of underwater detection models, we propose an improved YOLOv7 model. We introduce a multi-granularity feature attention method based on the Efficient Channel Attention (ECA) to help the model better adapt to the diverse conditions in the underwater environment, reducing focus on redundant information. Utilizing coordinate convolution provides the network with spatial awareness of input image coordinates, enabling more effective localization of target objects and reducing interference from similar background elements. To accommodate the features of small and dense underwater targets, we use normalized Wasserstein distance to measure the similarity of bounding boxes. On the Underwater Robot Picking Contest 2019 (URPC 2019) dataset, the mean Average Precision (mAP) of our improved network has reached 86.19%, which represents a 1.57% increase compared to the original YOLOv7 network. Additionally, the frames per second (fps) has achieved 124, surpassing the performance of the original network. This improvement is significantly superior to conventional target detection models, providing a faster and more accurate advantage for underwater target detection tasks in complex underwater environments.

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Data availability

The datasets used and analyzed during the current study are publicly available and can be accessed at http://en.urpc.org.cn. The relevant data sources and links are also cited in the manuscript.

Notes

  1. Underwater Robot Professional Contest: http://en.urpc.org.cn.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 62202499, the State Key Program of National Natural Science Foundation of China under Grant No. 62133016 and the Changsha Soft Science Research Program under Grant No. kh2302054.

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Authors and Affiliations

  1. School of Automation, Central South University, Changsha, 410083, Hunan, China

    Qingqi Wu, Lihui Cen & Xiaofang Chen

  2. School of Computer Science, Central South University, Changsha, 410083, Hunan, China

    Shichao Kan

  3. College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha, 410003, Hunan, China

    Yongping Zhai

  4. School of Economics and Management, Changsha University, Changsha, 410022, Hunan, China

    Hong Zhang

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  1. Qingqi Wu
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Correspondence to Lihui Cen.

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Wu, Q., Cen, L., Kan, S. et al. Real-time underwater target detection based on improved YOLOv7. J Real-Time Image Proc 22, 43 (2025). https://doi.org/10.1007/s11554-025-01621-1

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