[1] Yang L, Su J, Huang H et al. SAR ship detection based on convolutional neural network with deep multiscale feature fusion[J]. Acta Optica Sinica, 40, 0215002(2020).

[2] Zhou L Z, Zheng G, Yang J S et al. Sea surface wind speed retrieval from textures in synthetic aperture radar imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 4200911(2022).

[3] He J L, Wang Y H, Liu H W et al. A novel automatic PolSAR ship detection method based on superpixel-level local information measurement[J]. IEEE Geoscience and Remote Sensing Letters, 15, 384-388(2018).

[4] Yang H Y, Cao Z J, Cui Z Y et al. Saliency detection of targets in polarimetric SAR images based on globally weighted perturbation filters[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 147, 65-79(2019).

[5] Liu T, Yang Z Y, Yang J et al. CFAR ship detection methods using compact polarimetric SAR in a K-wishart distribution[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 3737-3745(2019).

[6] Carion N, Massa F, Synnaeve G et al. End-to-end object detection with transformers[EB/OL]. https:∥arxiv.org/abs/2005.12872

[7] Roh B, Shin J, Shin W et al. Sparse DETR: efficient end-to-end object detection with learnable sparsity[EB/OL]. https:∥arxiv.org/abs/2111.14330

[8] Zhao Y A, Lü W Y, Xu S L et al. DETRs Beat YOLOs on real-time object detection[EB/OL]. https:∥arxiv.org/abs/2304.08069

[9] He K M, Gkioxari G, Dollár P et al. Mask R-CNN[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 386-397(2020).

[10] Ren S Q, He K M, 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, 39, 1137-1149(2017).

[11] Pang J M, Chen K, Shi J P et al. Libra R-CNN: towards balanced learning for object detection[C], 821-830(2019).

[12] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C], 779-788(2016).

[13] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C], 6517-6525(2017).

[14] Redmon J, Farhadi A. YOLOv3: an incremental improvement[EB/OL]. https:∥arxiv.org/abs/1804.02767

[15] 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[C], 7464-7475(2023).

[16] Gong S R, Xu S J, Zhou L F et al. Deformable atrous convolution nearshore SAR small ship detection incorporating mixed attention[J]. Journal of Image and Graphics, 27, 3663-3676(2022).

[17] Ke X, Zhang X L, Zhang T W. GCBANet: a global context boundary-aware network for SAR ship instance segmentation[J]. Remote Sensing, 14, 2165(2022).

[18] Zhang T W, Zhang X L. A full-level context squeeze-and-excitation ROI extractor for SAR ship instance segmentation[J]. IEEE Geoscience and Remote Sensing Letters, 19, 4506705(2022).

[19] Tan X D, Peng H. Improved YOLOv5 ship target detection in SAR image[J]. Computer Engineering and Applications, 58, 247-254(2022).

[20] Hou Q B, Zhou D Q, Feng J S. Coordinate attention for efficient mobile network design[C], 13708-13717(2021).

[21] He K M, Zhang X, Ren S Q et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1904-1916(2014).

[22] Ouyang D L, He S, Zhang G Z et al. Efficient multi-scale attention module with cross-spatial learning[C]. Greece, 4-10(2023).

[23] Tian Z, Shen C H, Chen H et al. FCOS: a simple and strong anchor-free object detector[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 1922-1933(2022).

[24] Zheng Z H, Wang P, Liu W et al. Distance-IoU loss: faster and better learning for bounding box regression[EB/OL]. https:∥arxiv.org/abs/1911.08287

[25] Gevorgyan Z. SIoU loss: more powerful learning for bounding box regression[EB/OL]. https:∥arxiv.org/abs/2205.12740

[26] Zhang H, Zhang S J. Shape-IoU: more accurate metric considering bounding box shape and scale[EB/OL]. https:∥arxiv.org/abs/2312.17663

[27] Zhang H, Zhang S J. Focaler-IoU: more focused intersection over union loss[EB/OL]. https:∥arxiv.org/abs/2401.10525

[28] Wei S J, Zeng X F, Qu Q Z et al. HRSID: a high-resolution SAR images dataset for ship detection and instance segmentation[J]. IEEE Access, 8, 120234-120254(2020).

[29] Zhang T W, Zhang X L, Li J W et al. SAR ship detection dataset (SSDD): official release and comprehensive data analysis[J]. Remote Sensing, 13, 3690(2021).

[30] Zheng H K, Xue X R, Yue R et al. SAR image ship target detection based on receptive field enhancement module and cross-layer feature fusion[J]. Electronics, 13, 167(2023).

[31] He J Y, Su N, Xu C A et al. From optical to SAR: a SAR ship detection algorithm based on multi-level cross-modality alignment[J]. National Remote Sensing Bulletin, 28, 1789-1801(2024).