YOLO-DDE: A Method for Small Ship Detection in SAR Images
DOI:
https://doi.org/10.6919/ICJE.202507_11(7).0027Keywords:
Target Detection; YOLOv11; SAR Imagery; Adaptively Spatial Feature Fusion.Abstract
Aiming at the problems of small targets, weak feature expression and strong background interference in satellite SAR images, this paper proposes a new target detection method based on the improved YOLOv11s architecture to improve the detection performance. The algorithm adds multi-scale convolutional blocks (MSCB) and adopts deformable attention mechanism (DAttention) to expand the receptive field of the backbone network. In addition, an efficient multi-scale attention module (EMA) is integrated in the small target detection layer of the feature fusion network to improve the accuracy of feature fusion. These improvements significantly enhance the network's ability to detect small-sized targets. The effectiveness of the proposed method is evaluated on a public SAR image dataset: High Resolution SAR Image Dataset (HRSID). Experimental results show that compared with YOLOv11, the proposed method YOLO-DDE improves the average precision (AP50) by 2.1% on the HRSID dataset.
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[1] Zhang C, Liu P, Wang H, et al. A review of recent advance of ship detection in single-channel SAR images[J]. Waves in Random and Complex Media, 2023, 33(5-6): 1442-1473.
[2] Luo R, Chen L, Xing J, et al. A fast aircraft detection method for SAR images based on efficient bidirectional path aggregated attention network[J]. Remote Sensing, 2021, 13(15): 2940.
[3] Chang S, Deng Y, Zhang Y, et al. An advanced echo separation scheme for space-time waveform-encoding SAR based on digital beamforming and blind source separation[J]. Remote Sensing, 2022, 14(15): 3585.
[4] Yang, X., Sun, H., Fu, K., et al. Ship detection from remote sensing images using domain adaptive YOLO network. Remote Sensing, 2020, 12(3): 307.
[5] Marino, A., Dalla Mura, M., & Poggi, G. A novel approach for ship detection in SAR imagery based on wavelet domain and fuzzy logic. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(5): 2703–2714.
[6] Ren S, He K, Girshick R, et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 39(6): 1137–1149. DOI: 10.1109/TPAMI.2016.2577031.
[7] Liu W, Anguelov D, Erhan D, et al. Ssd: Single shot multibox detector[C]//Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14. Springer International Publishing, 2016: 21-37.
[8] Redmon J, Divvala S, Girshick R, et al. You only look once: Unified, real-time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 779-788.
[9] Rahman M M, Munir M, Marculescu R. Emcad: Efficient multi-scale convolutional attention decoding for medical image segmentation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024: 11769-11779
[10] Xia Z, Pan X, Song S, et al. Vision transformer with deformable attention[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022: 4794-4803.
[11] Ouyang D, He S, Zhang G, et al. Efficient multi-scale attention module with cross-spatial learning[C]//ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023: 1-5.
[12] Wei S, Zeng X, Qu Q, et al. HRSID: A high-resolution SAR images dataset for ship detection and instance segmentation[J]. Ieee Access, 2020, 8: 120234-120254.
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