[1] 张上, 陈益方, 王申涛, 等. 基于YOLOv5的改进舰船目标检测算法[J]. 电光与控制, 2023, 30(12): 66-72.
ZHANG S, CHEN Y F, WANG S T, et al. An improved ship target detection algorithm based on YOLOv5[J]. Electronics Optics & Control, 2023, 30(12): 66-72.
[2] LI W, MA P, WANG H, et al. SAR-TSCC: a novel approach for long time series SAR image change detection and pattern analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 1-16.
[3] HAMIDI E, PETER B G, MU?OZ D F, et al. Fast flood extent monitoring with SAR change detection using Google earth engine[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 1-19.
[4] LI S, WANG Y, CAI H, et al. MF-SRCDNet: multi-feature fusion super-resolution building change detection framework for multi-sensor high-resolution remote sensing imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2023, 119: 103303.
[5] 肖振久, 林渤翰, 曲海成. 改进YOLOv7的SAR舰船检测算法[J]. 计算机工程与应用, 2023, 59(15): 243-252.
XIAO Z J, LIN B H, QU H C. Improved SAR ship detec-tion algorithm for YOLOv7[J]. Computer Engineering and Applications, 2023, 59(15): 243-252.
[6] SHAO Z, ZHANG X, WEI S, et al. Scale in scale for SAR ship instance segmentation[J]. Remote Sensing, 2023, 15(3): 629.
[7] YASIR M, ZHAN L, LIU S, et al. Instance segmentation ship detection based on improved Yolov7 using complex background SAR images[J]. Frontiers in Marine Science, 2023, 10: 1113669.
[8] 严春满, 王铖. 基于选择性坐标注意力的SAR图像舰船目标检测[J]. 电子学报, 2023, 51(9): 2481-2491.
YAN C M, WANG C. Ship target detection in SAR image based on selective coordinate attention[J]. Acta Electronica Sinica, 2023, 51(9): 2481-2491.
[9] 张帆, 陆圣涛, 项德良, 等. 一种改进的高分辨率SAR图像超像素CFAR舰船检测算法[J]. 雷达学报, 2023, 12(1): 120-139.
ZHANG F, LU S T, XIANG D L, et al. An improved superpixel-based CFAR method for high-resolution SAR image ship target detection[J]. Journal of Radars, 2023, 12(1): 120-139.
[10] REN S, HE K, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems, 2015.
[11] KANG M, LENG X, LIN Z, et al. A modified faster R-CNN based on CFAR algorithm for SAR ship detection[C]//Proceedings of the 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), 2017: 1-4.
[12] ZHANG T, ZHANG X. ShipDeNet-20: an only 20 convolu-tion layers and<1-MB lightweight SAR ship detector[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 18(7): 1234-1238.
[13] AN Q, PAN Z, LIU L, et al. DRBox-v2: an improved detector with rotatable boxes for target detection in SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11): 8333-8349.
[14] DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16x16 words: transformers for image recog-nition at scale[J]. arXiv:2010.11929, 2020.
[15] XIA R, CHEN J, HUANG Z, et al. CRTransSar: a visual transformer based on contextual joint representation learning for SAR ship detection[J]. Remote Sensing, 2022, 14(6): 1488.
[16] WEI S, ZENG X, QU Q, et al. HRSID: a high-resolution SAR images dataset for ship detection and instance segmen-tation[J]. IEEE Access, 2020, 8: 120234-120254.
[17] GAO F, ZHONG F, SUN J, et al. BBox-Free SAR ship instance segmentation method based on Gaussian heatmap[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 5206218.
[18] YANG X, ZHANG Q, DONG Q, et al. Ship instance segmentation based on rotated bounding boxes for SAR images[J]. Remote Sensing, 2023, 15(5): 1324.
[19] SU H, WEI S, LIU S, et al. HQ-ISNet: high-quality instance segmentation for remote sensing imagery[J]. Remote Sensing, 2020, 12(6): 989.
[20] ZHAO D, ZHU C, QI J, et al. Synergistic attention for ship instance segmentation in SAR images[J]. Remote Sensing, 2021, 13(21): 4384.
[21] GAO F, HUO Y, WANG J, et al. Anchor-free SAR ship instance segmentation with centroid-distance based loss[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 11352-11371.
[22] DING X, ZHANG X, MA N, et al. Repvgg: making VGG-style convnets great again[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 13733-13742.
[23] WANG J, CHEN K, XU R, et al. Carafe++: unified content-aware reassembly of features[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 44(9): 4674-4687.
[24] QIAO S, CHEN L C, YUILLE A. Detectors: detecting objects with recursive feature pyramid and switchable atrous convolution[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 10213-10224.
[25] WANG X, ZHANG R, KONG T, et al. Solov2: dynamic and fast instance segmentation[C]//Advances in Neural Information Processing Systems, 2020: 17721-17732.
[26] BOLYA D, ZHOU C, XIAO F, et al. Yolact: real-time instance segmentation[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019: 9157-9166.
[27] HE K, GKIOXARI G, DOLLáR P, et al. Mask R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 2961-2969.
[28] HUANG Z, HUANG L, GONG Y, et al. Mask scoring R-CNN[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 6409-6418.
[29] CAI Z, VASCONCELOS N. Cascade R-CNN: high quality object detection and instance segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 43(5): 1483-1498.
[30] LIU Z, MAO H, WU C Y, et al. A convnet for the 2020s[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 11976-11986.
[31] LYU C, ZHANG W, HUANG H, et al. Rtmdet: an empirical study of designing real-time object detectors[J]. arXiv:2212.07784, 2022. |