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FS-YOLO: a multi-scale SAR ship detection network in complex scenes

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Abstract

Ship image recognition by synthetic aperture radar (SAR) is a crucial technology for intelligent shipping and maritime safety monitoring. However, existing ship detection algorithms are ineffective in detecting multi-scale ships in complex scenes. Therefore, we propose an FS-YOLO for detecting ship targets in complex backgrounds. Firstly, we design a feature enhancement module (FEM) using dilated convolution to enhance the feature-proposing ability of the backbone network. In addition, we propose a spatial channel pooling module combined with enhanced spatial pyramid pooling (ESPPCSPC) using channel attention to improve the expressive capability of the network. In addition, we achieve weighted feature fusion of different feature layers through a heightened bidirectional feature pyramid network structure (HPAN). Finally, we design a new confidence loss function to improve the robustness of the model to multi-scale targets. The experimental results show that the mAP50 values of FS-YOLO reach 96.9% and 89.5% on SSDD and HRSID datasets, respectively. Meanwhile, by comparing the performance of FS-YOLO with some commonly used detection algorithms, we discover that FS-YOLO has higher detection accuracy and good generalization.

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Data availability

All data included in this study are available upon request by contact with the corresponding author.

references

  1. Xu, G., Zhang, B.J., Yu, H.W., Chen, J.L., Xing, M.D., Hong, W.: Sparse synthetic aperture radar imaging from compressed sensing and machine learning: theories, applications, and trends. IEEE Geosci. Remote Sens. Mag. 10, 32–69 (2022)

    Article  Google Scholar 

  2. Cheney, M.: Introduction to Synthetic Aperture Radar (SAR) and SAR Interferometry. Approximation theory x, (2022).

  3. Zhou, S.C., Zhang, M., Wu, L., Yu, D.H., Li, J.J., Fan, F., Liu, Y., Zhang, L.Y.: SAR ship detection network based on global context and multi-scale feature enhancement. Signal Image Video Process. 18(3), 2951–2964 (2024)

    Article  Google Scholar 

  4. Ju, M.R., Niu, B.N., Zhang, B.: SAR image generation method for oriented ship detection via generative adversarial networks. SIViP 18, 589–596 (2024)

    Article  Google Scholar 

  5. Yang, Z.Q., Lai, Y.P., Zhou, H., Tian, Y.W., Qin, Y., Lv, Z.W.: Improving ship detection based on decision tree classification for high frequency surface wave radar. J. Marine Sci. Eng. 11(3), 493 (2023)

    Article  Google Scholar 

  6. Zhang, L., Zhang, Z.J., Lu, S.T., Xiang, D.L., Su, Y.: Fast superpixel-based non-window CFAR ship detector for SAR imagery. Remote Sens. 14(9), 2092 (2022)

    Article  Google Scholar 

  7. Bezerra, D.X., Lorenzzetti, J.A., Paes, R.L.: Marine environmental impact on CFAR ship detection as measured by wave age in SAR images. Remote Sens. 15(13), 3441 (2023)

    Article  Google Scholar 

  8. Zou, Z.X., Chen, K.Y., Shi, Z.W., Guo, Y.H., Ye, J.P.: Object detection in 20 years: A survey. Proc. IEEE 111, 257–276 (2023)

    Article  Google Scholar 

  9. Minaee, S., Kalchbrenner, N., Cambria, E., Nikzad, N., Chenaghlu, M., Gao, J.F.: Deep learning-based text classification: A comprehensive review. ACM Comput. Surv. 54(3), 1–4 (2022)

    Article  Google Scholar 

  10. Zhou, T.F., Porikli, F., Crandall, D.J., Van Gool, L., Wang, W.G.: A survey on deep learning technique for video segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 45, 7099–7122 (2023)

    Article  Google Scholar 

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

    Article  Google Scholar 

  12. He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)

    Article  Google Scholar 

  13. Nie, X., Duan, M., Ding, H., Hu, B., Wong, E.K.: Attention Mask R-CNN for ship detection and segmentation from remote sensing images. IEEE Access 8, 9325–9334 (2020)

    Article  Google Scholar 

  14. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, 21–37 (2016)

  15. Tian, Z., Shen, C., Chen, H., He, T.: FCOS: Fully Convolutional One-Stage Object Detection. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), (2020)

  16. Redmon, J., Farhadi, A:YOLOv3: An Incremental Improvement. arXiv preprint arXiv: 1804.02767, (2018)

  17. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.:YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv preprint arXiv: 2004.10934 (2020)

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

  19. Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. Proceedings of the IEEE conference on computer vision and pattern recognition, 2117–2125(2017)

  20. Tan, M., Pang, R., Le, Q.V.: EfficientDet: Scalable and Efficient Object Detection. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2020)

  21. Ghaisi, G., Lin, T.-Y., Pang, R., NAS-FPN, Q.V.L.: Learning scalable feature pyramid architecture for object detection. Proceedings of the IEEE computer vision and pattern recognition, 7029–7038

  22. Hu, J., Shen, L., Sun, G.: Squeeze-and-Excitation Networks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2018)

  23. Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 13713–13722(2021)

  24. Wang, Q., Shen, F.Y., Cheng, L.F., Jiang, J.F., He, G.H., Sheng, W.G., Jing, N.F., Mao, Z.G.: Ship detection based on fused features and rebuilt YOLOv3 networks in optical remote-sensing images. Int. J. Remote Sens. 42, 520–536 (2021)

    Article  Google Scholar 

  25. Yang, X., Zhang, X., Wang, N.N., Gao, X.B.: 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)

    Google Scholar 

  26. Zhang, L.M., Chu, Z.Y., Zou, B.: IEEE Multi Scale Ship Detection Based on Attention and Weighted Fusion Model for High Resolution SAR Images. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 631–634(2022)

  27. Deng, Y.W., Guan, D.H., Chen, Y.Y., Yuan, W.W., Ji, J.M., Wei, M.Q.: IEEE SAR-SHIPNET: SAR-ship detection neural network via bidirectional coordinate attention and multi-resolution feature fusion. 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3973–3977(2022)

  28. Tang, G., Zhao, H.R., Claramunt, C., Zhu, W.D., Wang, S.M., Wang, Y.D., Ding, Y.H.: PPA-Net: pyramid pooling attention network for multi-scale ship detection in SAR images. Remote Sens. 15, 11–2855 (2023)

    Article  Google Scholar 

  29. Suo, Z.L., Zhao, Y.B., Hu, Y.L.: An effective multi-layer attention network for SAR ship detection. J. Marine Sci. Eng. 11, 5–906 (2023)

    Article  Google Scholar 

  30. Tian, Y., Wang, X., Zhu, S.J., Xu, F., Liu, J.H.: LMSD-Net: a lightweight and high-performance ship detection network for optical remote sensing images. Remote Sens. 1, 17–4358 (2023)

    Google Scholar 

  31. Yu, F., Koltun, V., Funkhouser, T.: Dilated Residual Networks. IEEE Computer Society (2017)

  32. Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., Xu, C.: Ghostnet: More features from cheap operations. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 1580–1589 (2020)

  33. Rezatofighi, H., Tsoi, N., Gwak, J.Y., Sadeghian, A., Savarese, S.: Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (2019)

  34. Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D.: Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression. arXiv, (2019)

  35. Zhang, Y.F., Ren, W., Zhang, Z., Jia, Z., Wang, L., Tan, T.: Focal and efficient IOU loss for accurate bounding box regression. Neurocomputing 506, 146–157 (2022)

    Article  Google Scholar 

  36. He, J., Erfani, S., Ma, X., Bailey, J., Chi, Y., Hua, X.S.: $\alpha $-IoU: A family of power intersection over union losses for bounding box regression. Adv. Neural Inf. Process. Syst. 34, 20230–20242 (2021)

    Google Scholar 

  37. Zhang, T., Zhang, X., Li, J., Xu, X., Wang, B., Zhan, X., Xu, Y., Ke, X., Zeng, T., Su, H.: SAR ship detection dataset (SSDD): Official release and comprehensive data analysis. Remote Sens. 13, 18–3690 (2021)

    Google Scholar 

  38. Wei, S., Zeng, X., Qu, Q., Wang, M., Shi, J.: HRSID: a high-resolution SAR images dataset for ship detection and instance segmentation. IEEE Access 8, 1–1 (2020)

    Google Scholar 

  39. Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path Aggregation Network for Instance Segmentation. IEEE, (2018)

  40. Cui, Z., Li, Q., Cao, Z., Liu, N.: Dense attention pyramid networks for multi-scale ship detection in SAR images. IEEE Trans. Geosci. Remote Sens. 57(11), 8983–8997 (2019)

    Article  Google Scholar 

  41. Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: Deformable Convolutional Networks. IEEE, (2017)

  42. Ultralytics. The code address, https://github.com/ultralytics/yolov5 (2022)

  43. Ultralytics.The code address, https://github.com/ultralytics/ultralytics. (2023)

Download references

Acknowledgements

This work is supported by the project of National Key R&D Program of China (Grant: 2019YFE0105400) and Intelligent Situation Awareness System for Smart Ship (Grant: MC-201920-X01).

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Authors and Affiliations

  1. College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, 150001, China

    Shouwen Cai, Hao Meng, Ming Yuan & Junbao Wu

  2. Key Laboratory of Intelligent Technology and Application of Marine Equipment (Harbin Engineering University), Ministry of Education, Harbin, 150001, China

    Shouwen Cai, Hao Meng, Ming Yuan & Junbao Wu

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  1. Shouwen Cai
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  2. Hao Meng
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  3. Ming Yuan
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  4. Junbao Wu
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SC and HM are primarily responsible for writing the manuscript; JW and MY are mainly responsible for drawing diagrams. All authors reviewed the manuscript file.

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Correspondence to Hao Meng.

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Cai, S., Meng, H., Yuan, M. et al. FS-YOLO: a multi-scale SAR ship detection network in complex scenes. SIViP (2024). https://doi.org/10.1007/s11760-024-03212-2

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