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SAR ship detection network based on global context and multi-scale feature enhancement

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Abstract

With the rapid development of synthetic aperture radar (SAR) technology, SAR image ship detection plays a crucial role in fields such as marine environment monitoring and maritime traffic control. Due to the large-scale difference between ship targets and the small spatial feature information of targets in complex environments, the existing network models have low detection accuracy and cannot achieve satisfactory performance. Therefore, in this paper, a SAR ship detection network (FGNet) based on global context and multi-scale feature enhancement is proposed. The method adopts the global context block (GC block) to effectively model the global context information of the feature map and enhance the network’s focus on important features. In addition, we propose the feature enhancement module (FEM), which enhances the network’s feature representation capability for ships of different scales by designing a multi-branch structure with varying receptive field sizes. Experimental results on the SAR-Ship-Dataset and SSDD datasets show that the average accuracy of our method is 95% and 96.58%, respectively. Compared with other ship detection methods, the method has high detection accuracy and excellent generalization performance.

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Data availability

The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Yang, Xi., et al.: A robust one-stage detector for multiscale ship detection with complex background in massive SAR images. IEEE Trans. Geosci. Remote Sens. 60, 1–12 (2022). https://doi.org/10.1109/TGRS.2021.3128060

    Article  CAS  Google Scholar 

  2. Zhang, X., et al.: Multitask learning for ship detection from synthetic aperture radar images. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 14, 8048–8062 (2021). https://doi.org/10.1109/JSTARS.2021.3102989

    Article  ADS  Google Scholar 

  3. Idicula, S.M., Paul, B.: A novel sarnede method for real-time ship detection from synthetic aperture radar image. Multimed. Tools Appl. 81(12), 16921–16944 (2022)

    Article  Google Scholar 

  4. Niu, Y., et al.: Efficient encoder–decoder network with estimated direction for SAR ship detection. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2022). https://doi.org/10.1109/LGRS.2022.3145790

    Article  Google Scholar 

  5. Schou, J., et al.: CFAR edge detector for polarimetric SAR images. IEEE Trans. Geosci. Remote Sens. 41(1), 20–32 (2003). https://doi.org/10.1109/TGRS.2002.808063

    Article  ADS  Google Scholar 

  6. Kang, M., et al.: A modified faster R-CNN based on CFAR algorithm for SAR ship detection. In: 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), pp. 1–4 (2017). https://doi.org/10.1109/RSIP.2017.7958815

  7. Chen, S., Li, X.: A new CFAR algorithm based on variable window for ship target detection in SAR images. Signal Image Video Process. 13(4), 779–786 (2019)

    Article  Google Scholar 

  8. Cui, Y., Yang, J., Zhang, X.: New CFAR target detector for SAR images based on kernel density estimation and mean square error distance. J. Syst. Eng. Electron. 23(1), 40–46 (2012). https://doi.org/10.1109/JSEE.2012.00006

    Article  Google Scholar 

  9. Xu, P., et al.: On-board real-time ship detection in HISEA-1 SAR images based on CFAR and lightweight deep learning. Remote Sens. 13(10), 1995 (2021)

    Article  ADS  Google Scholar 

  10. Ren, S., et al.: Faster r-cnn: towards real-time object detection with region proposal networks. Adv. Neural. Inf. Process. Syst. 28, 45 (2015)

    Google Scholar 

  11. He, K., et al.: Mask r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)

  12. Dai, J., et al.: R-fcn: object detection via region-based fully convolutional networks. Adv. Neural. Inf. Process. Syst. 29, 32 (2016)

    Google Scholar 

  13. Liu, W., et al.: Ssd: single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14. Springer, pp. 21–37 (2016)

  14. Lin, T.Y., et al.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)

  15. Tan, M., Pang, R., Le, Q.V.: Efficient: scalable and efficient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10781–10790 (2020)

  16. Zhu, M., et al.: FSFADet: arbitrary-oriented ship detection for SAR images based on feature separation and feature alignment. Neural. Process. Lett. 54(3), 1995–2005 (2022)

    Article  Google Scholar 

  17. Zhang, M., et al.: Synthetic aperture radar ship detection in complex scenes based on multifeature fusion network. J. Appl. Remote. Sens. 17(1), 016511–016511 (2023)

    Article  ADS  Google Scholar 

  18. Zhao, Y., et al.: Attention receptive pyramid network for ship detection in SAR images. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 13, 2738–2756 (2020). https://doi.org/10.1109/JSTARS.2020.2997081

    Article  ADS  Google Scholar 

  19. Li, D., et al.: A novel multidimensional domain deep learning network for SAR ship detection. IEEE Trans. Geosci. Remote Sens. 60, 1–13 (2022). https://doi.org/10.1109/TGRS.2021.3062038

    Article  Google Scholar 

  20. Ge, Z., et al.: Yolox: exceeding yolo series in 2021 (2021). arXiv preprint arXiv:2107.08430

  21. Cao, Y., et al.: Gcnet: non-local networks meet squeeze-excitation networks and beyond. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp. 0–0 (2019)

  22. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

  23. Wang, X., et al.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2018)

  24. Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)

  25. Liu, S., Huang, D., et al.: Receptive field block net for accurate and fast object detection. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 385–400 (2018)

  26. Zheng, Z., et al.: Distance-IoU loss: faster and better learning for bounding box regression. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34(07), pp. 12993–13000 (2020)

  27. Li, J., Qu, C., Shao, J.: Ship detection in SAR images based on an improved faster R-CNN. In: 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA), pp. 1–6. IEEE (2017)

  28. Wang, Y., et al.: A SAR dataset of ship detection for deep learning under complex backgrounds. Remote Sens. 11(7), 765 (2019)

    Article  ADS  Google Scholar 

  29. Zhang, T., Zhang, X., Ke, X.: Quad-FPN: a novel quad feature pyramid network for SAR ship detection. Remote Sens. 13(14), 2771 (2021)

    Article  ADS  Google Scholar 

  30. Carion, N., et al.: End-to-end object detection with transformers. In: European Conference on Computer Vision, pp. 213–229. Springer (2020)

  31. Bochkovskiy, A., Wang, C.Y., Mark Liao, H.Y.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)

  32. 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. arXiv preprint arXiv:2207.02696 (2022)

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Acknowledgements

The author would like to thank the SAR-Ship-Dataset and SSDD datasets for their open access. This work is supported by the National Natural Science Foundation of China (62066036, 42106177) and the Natural Science Foundation of Inner Mongolia (2022LHMS06005).

Funding

This work is supported by the National Natural Science Foundation of China (62066036, 42106177) and the Natural Science Foundation of Inner Mongolia (2022LHMS06005).

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Authors and Affiliations

  1. Inner Mongolia University of Science and Technology, School of Information Engineering, Baotou, 014010, China

    Shichuang Zhou, Ming Zhang, Dahua Yu, Jianjun Li, Fei Fan, Yang Liu & Liyun Zhang

  2. Shandong First Medical University & Shandong Academy of Medical Sciences, School of Radiology, Tai’an, 271016, China

    Liang Wu

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  1. Shichuang Zhou
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  2. Ming Zhang
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Contributions

SZ and MZ were mainly responsible for writing the manuscript; FF, YL, and LZ mainly prepared Figs. 1 and 12; and all authors reviewed the manuscript documents.

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Correspondence to Ming Zhang.

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This study did not involve human or animal subjects, and thus, no ethical approval was required. The study protocol adhered to the guidelines established by the journal.

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Zhou, S., Zhang, M., Wu, L. et al. SAR ship detection network based on global context and multi-scale feature enhancement. SIViP 18, 2951–2964 (2024). https://doi.org/10.1007/s11760-023-02962-9

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