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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.

References

  1. Technical characteristics for an automatic identification system using time-division multiple access in the VHF maritime mobile band, Standard ITU-R M.1371. Available at http://www.itu.int/rec/R-REC-M.1371/en (2014)

  2. Zou, Z., Chen, K., Shi, Z., et al.: Object detection in 20 years: a survey. Proc. IEEE 111, 257–276 (2023)

    Article  Google Scholar 

  3. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)

  4. Girshick, R.: Fast R-CNN. In: Proceedings of IEEE Conference on Computer Vision (ICCV), pp. 1440–1448 (2015)

  5. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards realtime object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell.Intell. 39(6), 1137–1149 (2017)

    Article  Google Scholar 

  6. Liu, W., Anguelov, D., Erhan, D.: SSD: Single shot MultiBox detector. European Conference on Computer Vision, pp. 21–37 (2016)

  7. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)

  8. Redmon, J., Farhadi, A.: YOLO9000: Better, faster, stronger. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7263–7271 (2017)

  9. Redmon, J., Farhadi, A.: YOLOv3: An incremental improvement. Preprint at arXiv:1804.02767 (2018)

  10. Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: YOLOv4: Optimal speed and accuracy of object detection. Preprint at arXiv:2004.10934 (2020)

  11. Glenn, J., Alex, S., Jirka, B.: Ultralytics/YOLOv5:V6.0 (Versionv6.0). .Available at http://doi.org/10.5281/zenodo.63715 (2021)

  12. Wang, C.Y., Yeh, I.H., Liao, H.Y.M.: You only learn one representation: Unified network for multiple tasks. Preprint at arXiv:2105.04206 (2021)

  13. Ge, Z., Liu, S., Wang, F., et al.: Yolox: Exceeding yolo series in 2021. Preprint at arXiv:2107.08430 (2021)

  14. Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7464–7475 (2023)

  15. Howard, A.G., Zhu, M., Chen, B., et al.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. Preprint at arXiv:1704.04861 (2017)

  16. Sandler, M., Howard, A., Zhu, M., et al.: Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)

  17. Howard, A., Sandler, M., Chen, B., Wang, W., Chen, L.-C., Tan, M., Chu, G., Vasudevan, V., Zhu, Y., Pang, R., Adam, H., Le, Q.: Searching for MobileNetV3. In: Proceedings of IEEE/CVF International Conference on Computer Vision (ICCV), pp. 1314–1324 (2019)

  18. Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848–6856 (2018)

  19. Ma, N., Zhang, X.: ShuffleNet V2: Practical guidelines for efficient CNN architecture design. In: Proceedings of European Conference on Computer Vision (ECCV), pp. 122–138 (2018)

  20. Vasu, P.K.A., Gabriel, J., Zhu, J., et al.: MobileOne: an improved one millisecond mobile backbone. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7907–7917 (2023)

  21. Wang, C.-Y., Liao, H.-Y.M., Wu, Y.-H., Chen, P.-Y., Hsieh, J.-W., Yeh, I.-H.: CSPNet: A new backbone that can enhance learning capability of CNN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Work-shops (CVPRW), pp. 390–391 (2020)

  22. He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell.Intell. 37(9), 1904–1916 (2014)

    Article  Google Scholar 

  23. Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8759–8768 (2018)

  24. Zhang, M., Rong, X., Yu, X.: Light-SDNet: A lightweight CNN architecture for ship detection. IEEE Access 10, 86647–86662 (2022)

    Article  Google Scholar 

  25. Zheng, Y., Zhang, Y., Qian, L., et al.: A lightweight ship target detection model based on improved YOLOv5s algorithm. PLoS ONE 18(4), e0283932 (2023)

    Article  Google Scholar 

  26. Cen, J., Feng, H., Liu, X., et al.: An improved ship classification method based on YOLOv7 model with attention mechanism. Wireless Commun. Mobile Comput. 2023, 1 (2023)

    Article  Google Scholar 

  27. Li, D., Zhang, Z., Fang, Z., et al.: Ship detection with optical image based on CA-YOLO v3 Network. In: IEEE 3rd International Conference on Frontiers of Electronics, Information and Computation Technologies (ICFEICT), pp. 589–598 (2023)

  28. Qian, L., Zheng, Y., Cao, J., et al.: Lightweight ship target detection algorithm based on improved YOLOv5s. J. Real-Time Image Proc. 21(1), 1–15 (2024)

    Article  Google Scholar 

  29. Chen, J., Kao, S., He, H., et al.: Run, don't walk: chasing higher FLOPS for faster neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12021–12031 (2023)

  30. Vadera, S., Ameen, S.: Methods for pruning deep neural networks. IEEE Access 10, 63280–63300 (2022)

    Article  Google Scholar 

  31. Gholami A, Kim S, Dong Z, et al. A survey of quantization methods for efficient neural network inference. Low-Power Computer Vision, pp. 291–326 Chapman and Hall/CRC, Boca Raton (2022)

  32. Gou, J., Yu, B., Maybank, S.J., et al.: Knowledge distillation: a survey. Int. J. Comput. VisionComput. Vision 129, 1789–1819 (2021)

    Article  Google Scholar 

  33. Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., Xu, C.: GhostNet: More features from cheap operations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1577–1586 (2020)

  34. Yun, S., Han, D., Oh, S.J., et al.: Cutmix: Regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE/CVF international conference on computer vision, pp. 6023–6032 (2019)

  35. Zeng, G., Yu, W., Wang, R., et al.: Research on mosaic image data enhancement for overlapping ship targets. Preprint at arXiv:2105.05090 (2021)

  36. Stemmer, U.: Locally private k-means clustering. J. Mach. Learn. Res. 22(1), 7964–7993 (2021)

    MathSciNet  Google Scholar 

  37. Shao, Z., Wu, W., Wang, Z., Du, W., Li, C.: SeaShips: A large-scale precisely annotated dataset for ship detection. IEEE Trans. Multimedia 20(10), 2593–2604 (2018)

    Article  Google Scholar 

  38. Gao, X., Sun, W.: Ship object detection in one-stage framework based on Swin-Transformer. In: Proceedings of the 2022 5th International Conference on Signal Processing and Machine Learning, pp. 189–196 (2022)

  39. Zhu, L., Geng, X., Li, Z., Liu, C.: Improving YOLOv5 with attention mechanism for detecting boulders from planetary images. Remote Sens. 13(18), 3776 (2021)

    Article  Google Scholar 

  40. Baltrušaitis, T., Ahuja, C., Morency, L.P.: Multimodal machine learning: a survey and taxonomy. IEEE Trans. Pattern Anal. Mach. Intell.Intell. 41(2), 423–443 (2018)

    Article  Google Scholar 

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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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