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Efficient detection method of deep-sea netting breakage based on attention and focusing on receptive-field spatial feature

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Abstract

Fish escapes due to breaches in deep-sea netting can affect local ecosystems. To accurately and quickly detect broken netting, we propose YOLOv7-net, an efficient deep-sea netting breakage detection method based on attention and focusing on the receptive-field spatial feature. First, Bi-level Routing Attention (BRA) is introduced to enhance the acquisition of feature information at different scales. Second, a new coordinated attention module (RFCAConv) that focuses on the spatial features of the receptive field is used to capture more detailed feature information. Finally, a new network module called CFE that integrates efficient channel attention (ECA) and FasterNet during cross-stage connections is designed, enhancing the ability of the network to express features while reducing the number of required parameters and computational complexity. The results obtained on a self-constructed broken netting dataset show that the precision, recall, AP, F1 score and detection speed of YOLOv7-net are 2.8%, 1.8%, 2.4%, 2%, and 8.92 fps higher than those of YOLOv7, respectively, and the proposed approach can be specifically used to identify deep-sea netting damage. Our method improves the efficiency of broken netting detection in complex marine environments, providing new insights into the development of mariculture equipment and the protection of ecosystems.

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Data availability

The broken netting datasets generated during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

This research was supported by the Guangdong Provincial Special Project for Marine Economic Development (Six Major Marine Industries) (GDNRC [2021] 42) and the Zhanjiang Key Laboratory of Modern Marine Fishery Equipment (2021A05023).

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Authors and Affiliations

  1. School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang, 524088, Guangdong Province, China

    Guoyan Yu, Jinping Su, Yingtong Luo, Zejia Chen, Qibo Chen & Shuaixing Chen

  2. Guangdong Marine Equipment and Manufacturing Engineering Technology Research Centre, Guangdong Ocean University, Zhanjiang, 524088, Guangdong Province, China

    Guoyan Yu, Jinping Su & Yingtong Luo

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  1. Guoyan Yu
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  2. Jinping Su
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  3. Yingtong Luo
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  4. Zejia Chen
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  5. Qibo Chen
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  6. Shuaixing Chen
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Contributions

GYY provided project support. JPS proposed an idea for the improvement algorithm and wrote the main manuscript text. YTL verified the results and reviewed the manuscript. ZJC, QBC, and SXC Collected a dataset of broken netting.

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Correspondence to Jinping Su.

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Yu, G., Su, J., Luo, Y. et al. Efficient detection method of deep-sea netting breakage based on attention and focusing on receptive-field spatial feature. SIViP 18, 1205–1214 (2024). https://doi.org/10.1007/s11760-023-02806-6

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  • DOI: https://doi.org/10.1007/s11760-023-02806-6

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