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LSDNet: a lightweight ship detection network with improved YOLOv7

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Abstract

Accurate ship detection is critical for maritime transportation security. Current deep learning-based object detection algorithms have made marked progress in detection accuracy. However, these models are too heavy to be applied in mobile or embedded devices with limited resources. Thus, this paper proposes a lightweight convolutional neural network shortened as LSDNet for mobile ship detection. In the proposed model, we introduce Partial Convolution into YOLOv7-tiny to reduce its parameter and computational complexity. Meanwhile, GhostConv is introduced to further achieve lightweight structure and improve detection performance. In addition, we use Mosaic-9 data-augmentation method to enhance the robustness of the model. We compared the proposed LSDNet with other approaches on a publicly available ship dataset, SeaShips7000. The experimental results show that LSDNet achieves higher accuracy than other models with less computational cost and parameters. The test results also suggest that the proposed model can meet the requirements of real-time applications.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61401127, Provincial Natural Science Foundation under Grant LH2022F038 and Cultivation Project of National Natural Science Foundation of Harbin Normal University under Grant XPPY202208.

Funding

National Natural Science Foundation of China, 61401127, Provincial Natural Science Foundation, LH2022F038, Cultivation Project of National Natural Science Foundation, XPPY202208.

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Authors and Affiliations

  1. School of Physics and Electronic Engineering, Harbin Normal University, Harbin, 150025, China

    Cui Lang, Xiaoyan Yu & Xianwei Rong

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  1. Cui Lang
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  2. Xiaoyan Yu
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  3. Xianwei Rong
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Contributions

Cui Lang and Xiaoyan Yu wrote the main manuscript text and prepared all the figures and tables, Xianwei Rong made the data curation, modified and edited the original draft. All authors reviewed the manuscript.

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Correspondence to Xiaoyan Yu.

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Lang, C., Yu, X. & Rong, X. LSDNet: a lightweight ship detection network with improved YOLOv7. J Real-Time Image Proc 21, 60 (2024). https://doi.org/10.1007/s11554-024-01441-9

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