[2] LI J W, CHEN J, CHENG P, et al. A survey on deep-learning-based real-time SAR ship detection [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16:3218-3247.
[3] AO W, XU F, LI Y C, et al. Detection and discrimination of ship targets in complex background from spaceborne ALOS-2 SAR images [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11:536-550.
[4] WANG Y H, LIU H W. PolSAR ship detection based on superpixel-level scattering mechanism distribution features [J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(8):1780-1784.
[5] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation [C]//2014 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Columbus: IEEE, 2014: 580-587.
[6] GIRSHICK R. Fast R-CNN [C]//2015 IEEE International Conference on Computer Vision (ICCV). Santiago: IEEE, 2015: 1440-1448.
[7] 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, 2017, 39(6): 1137-1149.
[8] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector [C]//Computer Vision-ECCV 2016. Cham: Springer, 2016: 21-37.
[9] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection [C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 779-788.
[10] KE X, ZHANG X L, ZHANG T W, et al. SAR ship detection based on swin transformer and feature enhancement feature pyramid network [C]//IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium(IGARSS). Kuala Lumpur: IEEE, 2022: 2163-2166.
[11] SUN B W, WANG X F, OAD A, et al. Automatic ship object detection model based on YOLOv4 with transformer mechanism in remote sensing images [J]. Applied Sciences, 2023, 13(4):2488.
[15] HU Q, HU S H, LIU S Q. BANet: a balance attention network for anchor-free ship detection in SAR images [J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60. doi: 10.1109/TGRS.2022.3146027.
[16] WANG W H, DAI J F, CHEN Z, et al. InternImage: exploring large-scale vision foundation models with deformable convolutions [C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Vancouver: IEEE, 2023. doi: 10.1109/CVPR52729.2023.01385.
[17] 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, 2020, 8: 120234-120254.
[18] TANG F, HUANG Q, WANG J, et al. DuAT: dual-aggregation transformer network for medical image segmentation [R]. Los Alamos: arXiv Preprint, 2022: arXiv: 2212. 11677.
[19] CARION N, MASSA F, SYNNAEVE G, et al. End-to-end object detection with transformers [R]. Los Alamos: arXiv Preprint, 2020: arXiv: 2005. 12872.
[20] TIAN Z, SHEN C H, CHEN H, et al. FCOS: fully convolutional one-stage object detection [C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul: IEEE, 2019: 9626-9635.
[21] BOCHKOVSKIY A, WANG C Y, LIAO H-Y M. YOLOv4: optimal speed and accuracy of object detection [R]. Los Alamos: arXiv Preprint, 2020: arXiv: 2004. 10934.
[22] GE Z, LIU S T, WANG F, et al. YOLOX: exceeding YOLO series in 2021 [R]. Los Alamos: arXiv Preprint, 2021: arXiv: 2107. 08430
[23] 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]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Vancouver: IEEE, 2023. doi: 10.1109/CVPR52729.2023.00721.
