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AMANet: Advancing SAR Ship Detection with Adaptive Multi-Hierarchical Attention Network (2401.13214v1)

Published 24 Jan 2024 in cs.CV, cs.AI, and cs.LG

Abstract: Recently, methods based on deep learning have been successfully applied to ship detection for synthetic aperture radar (SAR) images. Despite the development of numerous ship detection methodologies, detecting small and coastal ships remains a significant challenge due to the limited features and clutter in coastal environments. For that, a novel adaptive multi-hierarchical attention module (AMAM) is proposed to learn multi-scale features and adaptively aggregate salient features from various feature layers, even in complex environments. Specifically, we first fuse information from adjacent feature layers to enhance the detection of smaller targets, thereby achieving multi-scale feature enhancement. Then, to filter out the adverse effects of complex backgrounds, we dissect the previously fused multi-level features on the channel, individually excavate the salient regions, and adaptively amalgamate features originating from different channels. Thirdly, we present a novel adaptive multi-hierarchical attention network (AMANet) by embedding the AMAM between the backbone network and the feature pyramid network (FPN). Besides, the AMAM can be readily inserted between different frameworks to improve object detection. Lastly, extensive experiments on two large-scale SAR ship detection datasets demonstrate that our AMANet method is superior to state-of-the-art methods.

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References (65)
  1. S. Bhattacharjee, P. Shanmugam, and S. Das, “A deep-learning-based lightweight model for ship localizations in sar images,” IEEE Access, 2023.
  2. Y. Gao, Z. Wu, M. Ren, and C. Wu, “Improved yolov4 based on attention mechanism for ship detection in sar images,” IEEE Access, vol. 10, pp. 23 785–23 797, 2022.
  3. L. Han, D. Ran, W. Ye, W. Yang, and X. Wu, “Multi-size convolution and learning deep network for sar ship detection from scratch,” IEEE Access, vol. 8, pp. 158 996–159 016, 2020.
  4. J. Liu, F. Shen, M. Wei, Y. Zhang, H. Zeng, J. Zhu, and C. Cai, “A large-scale benchmark for vehicle logo recognition,” in 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC).   IEEE, 2019, pp. 479–483.
  5. W. Fan, F. Zhou, X. Bai, M. Tao, and T. Tian, “Ship detection using deep convolutional neural networks for polsar images,” Remote Sensing, vol. 11, no. 23, p. 2862, 2019.
  6. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “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, 2016, pp. 21–37.
  7. S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Advances in neural information processing systems, vol. 28, 2015.
  8. T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2117–2125.
  9. S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia, “Path aggregation network for instance segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 8759–8768.
  10. M. Tan, R. Pang, and Q. V. Le, “Efficientdet: Scalable and efficient object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 10 781–10 790.
  11. M. Jaderberg, K. Simonyan, A. Zisserman et al., “Spatial transformer networks,” Advances in neural information processing systems, vol. 28, 2015.
  12. J. Hu, L. Shen, S. Albanie, G. Sun, and A. Vedaldi, “Gather-excite: Exploiting feature context in convolutional neural networks,” Advances in neural information processing systems, vol. 31, 2018.
  13. J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 7132–7141.
  14. Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, and Q. Hu, “Eca-net: Efficient channel attention for deep convolutional neural networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 11 534–11 542.
  15. S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, “Cbam: Convolutional block attention module,” in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 3–19.
  16. J. Park, S. Woo, J.-Y. Lee, and I. S. Kweon, “Bam: Bottleneck attention module,” arXiv preprint arXiv:1807.06514, 2018.
  17. W. Weng, W. Lin, F. Lin, J. Ren, and F. Shen, “A novel cross frequency-domain interaction learning for aerial oriented object detection,” in Chinese Conference on Pattern Recognition and Computer Vision (PRCV).   Springer, 2023, pp. 292–305.
  18. C. Qiao, F. Shen, X. Wang, R. Wang, F. Cao, S. Zhao, and C. Li, “A novel multi-frequency coordinated module for sar ship detection,” in 2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI).   IEEE, 2022, pp. 804–811.
  19. Z. Zhao, K. Ji, X. Xing, H. Zou, and S. Zhou, “Ship surveillance by integration of space-borne sar and ais–review of current research,” The Journal of Navigation, vol. 67, no. 1, pp. 177–189, 2014.
  20. A. Farina and F. A. Studer, “A review of cfar detection techniques in radar systems,” Microwave Journal, vol. 29, p. 115, 1986.
  21. M. Yasir, W. Jianhua, X. Mingming, S. Hui, Z. Zhe, L. Shanwei, A. T. I. Colak, and M. S. Hossain, “Ship detection based on deep learning using sar imagery: a systematic literature review,” Soft Computing, vol. 27, no. 1, pp. 63–84, 2023.
  22. J. Hu, Z. Huang, F. Shen, D. He, and Q. Xian, “A bag of tricks for fine-grained roof extraction,” in IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium.   IEEE, 2023.
  23. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779–788.
  24. J. Redmon and A. Farhadi, “Yolo9000: better, faster, stronger,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 7263–7271.
  25. ——, “Yolov3: An incremental improvement,” arXiv preprint arXiv:1804.02767, 2018.
  26. A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “Yolov4: Optimal speed and accuracy of object detection,” arXiv preprint arXiv:2004.10934, 2020.
  27. E. Hassan, Y. Khalil, and I. Ahmad, “Learning feature fusion in deep learning-based object detector,” Journal of Engineering, vol. 2020, pp. 1–11, 2020.
  28. J. Hu, Z. Huang, F. Shen, D. He, and Q. Xian, “A rubust method for roof extraction and height estimation,” in IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium.   IEEE, 2023.
  29. C. Yu, J. Wang, C. Peng, C. Gao, G. Yu, and N. Sang, “Bisenet: Bilateral segmentation network for real-time semantic segmentation,” in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 325–341.
  30. H. Wu, F. Shen, J. Zhu, H. Zeng, X. Zhu, and Z. Lei, “A sample-proxy dual triplet loss function for object re-identification,” IET Image Processing, vol. 16, no. 14, pp. 3781–3789, 2022.
  31. H. Zhao, Y. Zhang, S. Liu, J. Shi, C. C. Loy, D. Lin, and J. Jia, “Psanet: Point-wise spatial attention network for scene parsing,” in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 267–283.
  32. F. Shen, X. Du, L. Zhang, and J. Tang, “Triplet contrastive learning for unsupervised vehicle re-identification,” arXiv preprint arXiv:2301.09498, 2023.
  33. F. Shen, J. Zhu, X. Zhu, J. Huang, H. Zeng, Z. Lei, and C. Cai, “An efficient multiresolution network for vehicle reidentification,” IEEE Internet of Things Journal, vol. 9, no. 11, pp. 9049–9059, 2021.
  34. Q. Ming, Z. Zhou, L. Miao, H. Zhang, and L. Li, “Dynamic anchor learning for arbitrary-oriented object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 3, 2021, pp. 2355–2363.
  35. F. Shen, J. Zhu, X. Zhu, Y. Xie, and J. Huang, “Exploring spatial significance via hybrid pyramidal graph network for vehicle re-identification,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 8793–8804, 2021.
  36. F. Shen, Z. Wang, Z. Wang, X. Fu, J. Chen, X. Du, and J. Tang, “A competitive method for dog nose-print re-identification,” arXiv preprint arXiv:2205.15934, 2022.
  37. J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 764–773.
  38. F. Shen, X. Peng, L. Wang, X. Zhang, M. Shu, and Y. Wang, “Hsgm: A hierarchical similarity graph module for object re-identification,” in 2022 IEEE International Conference on Multimedia and Expo (ICME).   IEEE, 2022, pp. 1–6.
  39. F. Shen, M. Wei, and J. Ren, “Hsgnet: Object re-identification with hierarchical similarity graph network,” arXiv preprint arXiv:2211.05486, 2022.
  40. F. Shen, Y. Xie, J. Zhu, X. Zhu, and H. Zeng, “Git: Graph interactive transformer for vehicle re-identification,” IEEE Transactions on Image Processing, 2023.
  41. X. Li, Z. Zhong, J. Wu, Y. Yang, Z. Lin, and H. Liu, “Expectation-maximization attention networks for semantic segmentation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 9167–9176.
  42. Z. Huang, X. Wang, L. Huang, C. Huang, Y. Wei, and W. Liu, “Ccnet: Criss-cross attention for semantic segmentation,” in Proceedings of the IEEE/CVF international conference on computer vision, 2019, pp. 603–612.
  43. P. Ramachandran, N. Parmar, A. Vaswani, I. Bello, A. Levskaya, and J. Shlens, “Stand-alone self-attention in vision models,” Advances in neural information processing systems, vol. 32, 2019.
  44. F. Shen, X. Shu, X. Du, and J. Tang, “Pedestrian-specific bipartite-aware similarity learning for text-based person retrieval,” in Proceedings of the 31th ACM International Conference on Multimedia, 2023.
  45. G. Tang, H. Zhao, C. Claramunt, W. Zhu, S. Wang, Y. Wang, and Y. Ding, “Ppa-net: Pyramid pooling attention network for multi-scale ship detection in sar images,” Remote Sensing, vol. 15, no. 11, p. 2855, 2023.
  46. X. Li, D. Li, H. Liu, J. Wan, Z. Chen, and Q. Liu, “A-bfpn: An attention-guided balanced feature pyramid network for sar ship detection,” Remote Sensing, vol. 14, no. 15, p. 3829, 2022.
  47. L. Bai, C. Yao, Z. Ye, D. Xue, X. Lin, and M. Hui, “Feature enhancement pyramid and shallow feature reconstruction network for sar ship detection,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 1042–1056, 2023.
  48. S. Wei, H. Su, J. Ming, C. Wang, M. Yan, D. Kumar, J. Shi, and X. Zhang, “Precise and robust ship detection for high-resolution sar imagery based on hr-sdnet,” Remote Sensing, vol. 12, no. 1, p. 167, 2020.
  49. L. Zheng, L. Tan, L. Zhao, F. Ning, B. Xiao, and Y. Ye, “Sse-ship: A sar image ship detection model with expanded detection field of view and enhanced effective feature information,” Open Journal of Applied Sciences, vol. 13, no. 4, pp. 562–578, 2023.
  50. C. Chen, W. Zeng, X. Zhang, and Y. Zhou, “Cs n net: A remote sensing detection network breaking the second-order limitation of transformers with recursive convolutions,” IEEE Transactions on Geoscience and Remote Sensing, 2023.
  51. G. Yan, Z. Chen, Y. Wang, Y. Cai, and S. Shuai, “Lssdet: A lightweight deep learning detector for sar ship detection in high-resolution sar images,” Remote Sensing, vol. 14, no. 20, p. 5148, 2022.
  52. Z. Chen, C. Liu, V. Filaretov, and D. Yukhimets, “Multi-scale ship detection algorithm based on yolov7 for complex scene sar images,” Remote Sensing, vol. 15, no. 8, p. 2071, 2023.
  53. T. Zhang, X. Zhang, and X. Ke, “Quad-fpn: A novel quad feature pyramid network for sar ship detection,” Remote Sensing, vol. 13, no. 14, p. 2771, 2021.
  54. Y. Guo and L. Zhou, “Mea-net: a lightweight sar ship detection model for imbalanced datasets,” Remote Sensing, vol. 14, no. 18, p. 4438, 2022.
  55. Q. Hu, S. Hu, S. Liu, S. Xu, and Y.-D. Zhang, “Finet: A feature interaction network for sar ship object-level and pixel-level detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–15, 2022.
  56. K. Sun, Y. Liang, X. Ma, Y. Huai, and M. Xing, “Dsdet: A lightweight densely connected sparsely activated detector for ship target detection in high-resolution sar images,” Remote Sensing, vol. 13, no. 14, p. 2743, 2021.
  57. X. Sun, Y. Lv, Z. Wang, and K. Fu, “Scan: Scattering characteristics analysis network for few-shot aircraft classification in high-resolution sar images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–17, 2022.
  58. J. Lv, J. Chen, Z. Huang, H. Wan, C. Zhou, D. Wang, B. Wu, and L. Sun, “An anchor-free detection algorithm for sar ship targets with deep saliency representation,” Remote Sensing, vol. 15, no. 1, p. 103, 2023.
  59. J. Li, C. Qu, and J. Shao, “Ship detection in sar images based on an improved faster r-cnn,” in 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA).   IEEE, 2017, pp. 1–6.
  60. T. Zhang, X. Zhang, J. Li, X. Xu, B. Wang, X. Zhan, Y. Xu, X. Ke, T. Zeng, H. Su et al., “Sar ship detection dataset (ssdd): Official release and comprehensive data analysis,” Remote Sensing, vol. 13, no. 18, p. 3690, 2021.
  61. S. Wei, X. Zeng, Q. Qu, M. Wang, H. Su, and J. Shi, “Hrsid: A high-resolution sar images dataset for ship detection and instance segmentation,” Ieee Access, vol. 8, pp. 120 234–120 254, 2020.
  62. Y. Zhang, D. Han et al., “Swin-paff: A sar ship detection network with contextual cross-information fusion.” Computers, Materials & Continua, vol. 77, no. 2, 2023.
  63. H. Qu, R. Li, Y. Shan, and M. Wang, “Sw-net: anchor-free ship detection based on spatial feature enhancement and weight-guided fusion,” Signal, Image and Video Processing, pp. 1–15, 2023.
  64. F. Shen, X. He, M. Wei, and Y. Xie, “A competitive method to vipriors object detection challenge,” arXiv preprint arXiv:2104.09059, 2021.
  65. X. Fu, F. Shen, X. Du, and Z. Li, “Bag of tricks for “vision meet alage” object detection challenge,” in 2022 6th International Conference on Universal Village (UV).   IEEE, 2022, pp. 1–4.
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Authors (5)
  1. Xiaolin Ma (9 papers)
  2. Junkai Cheng (1 paper)
  3. Aihua Li (4 papers)
  4. Yuhua Zhang (3 papers)
  5. Zhilong Lin (1 paper)
Citations (1)
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