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Lightweight ship target detection algorithm based on improved YOLOv5s

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Abstract

Accurate identification of ship targets is the key technology of intelligent inland waterway navigation. Given the complicated navigation environment of inland waterway ships and model detection's low accuracy and efficiency, this paper proposes an enhanced detection algorithm MGS-YOLO based on YOLOv5s. Firstly, the original backbone network is replaced by the MobileNetv3 algorithm, and the improved network parameter is only 7.54 MB. Secondly, the Gated Convolution (GnConv) structure is introduced into the original feature fusion module, which effectively improves the spatial interaction ability of feature information at different levels and further reduced the computational complexity of the model. Finally, to further improve the training speed and reasoning accuracy of the model, the SCYLLA-IoU (SIoU) is introduced into MGS-YOLO to effectively solve the problem of mismatching in the direction between the real box and the regression box. The final results show that the mean Average Precision (mAP), F1, and Average Frames Per Second (AVGFPS) of MGS-YOLO reach 0.977, 0.95, and 95.24 on the established ship dataset. It means that MGS-YOLO does not lose prediction accuracy when reducing network parameters and it has certain real-time performance. Comparing with the current representative lightweight learning models YOLOv5s, YOLOv3-tiny, YOLOv4-tiny, and YOLOv7 with good performance, the MGS-YOLO model has higher detection accuracy and efficiency and provides certain technical support for the safety detection and management of inland ships.

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Data availability

The datasets analyzed during the current study are not publicly available, as the data also forms part of an ongoing study but are available from the corresponding author on reasonable request.

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Funding

This work was funded by National Natural Science Foundation of China with Grant number 51979215 and 52171350.

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Authors and Affiliations

  1. School of Navigation, Wuhan University of Technology, Wuhan, 430036, People’s Republic of China

    Long Qian, Yuanzhou Zheng, Jingxin Cao, Yong Ma, Yuanfeng Zhang & Xinyu Liu

  2. Hubei Key Laboratory of Inland Shipping Technology, Wuhan, 430036, People’s Republic of China

    Long Qian, Yuanzhou Zheng, Jingxin Cao, Yong Ma, Yuanfeng Zhang & Xinyu Liu

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  1. Long Qian
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  2. Yuanzhou Zheng
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  3. Jingxin Cao
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  4. Yong Ma
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  5. Yuanfeng Zhang
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  6. Xinyu Liu
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Contributions

LQ: Conceptualization, Formal analysis,Methodology, review and editing, Manuscript writing, original draft preparation. YZ: Conceptualization and Funding acquisition, review and editing. JC: Formal analysis,Methodology and Data curation, review and editing. YM: Data curation,Methodology and validation. YZ and XL: Data curation. All authors reviewed the manuscript.

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Correspondence to Yuanzhou Zheng.

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Qian, L., Zheng, Y., Cao, J. et al. Lightweight ship target detection algorithm based on improved YOLOv5s. J Real-Time Image Proc 21, 3 (2024). https://doi.org/10.1007/s11554-023-01381-w

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