[1] GIRSHICK R，DONAHUE J，DARRELL T，et al. Rich feature hierarchies for accurate object detection and semantic segmentation [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition（CVPR）. Columbus: IEEE，2014: 580-587.

[2] GIRSHICK R. Fast R-CNN [C]//2015 IEEE International Conference on Computer Vision (ICCV). Santiago: IEEE，2015: 1440-1448.

[3] 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（CVPR）. Las Vegas: IEEE，2016: 779-788.

[4] REDMON J，FARHADI A. YOLOv3: an incremental improvement [R]. Los Alamos: arXiv Preprint，2018: arXiv: 1804.02767.

[5] ZHU X K，LYU S C，WANG X，et al. TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios [C]//2021 IEEE/CVF International Conference on Computer Vision Workshops(ICCVW). Montreal: IEEE，2021: 2778-2788.

[6] REDMON J，FARHADI A. YOLO9000: better，faster，stronger [C]//2017 IEEE Conference on Computer Vision and Pattern Recognition（CVPR）. Honolulu: IEEE，2017: 6517-6525.

[7] LIU W，ANGUELOV D，ERHAN D，et al. SSD: single shot multibox detector [C]//European Conference on Computer Vision(ECCV). Cham: Springer，2016: 21-37.

[8] GUO W，YANG W，ZHANG H J，et al. Geospatial object detection in high resolution satellite images based on multi-scale convolutional neural network [J]. Remote Sensing，2018，10(1):131.

[11] ZHANG F，WANG X Y，ZHOU S L，et al. Arbitrary-oriented ship detection through center-head point extraction [J]. IEEE Transactions on Geoscience and Remote Sensing，2021，60:5612414.

[12] WANG K X，LIEW H J，ZOU Y T，et al. PaNet: few-shot image semantic segmentation with prototype alignment [C]//2019 IEEE/CVF International Conference on Computer Vision Workshops(ICCVW). Seoul: IEEE，2019: 9197-9206.

[13] LIN T Y，DOLLAR P，GIRSHICK R，et al. Feature pyramid networks for object detection [C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition（CVPR）. Honolulu: IEEE，2017: 936-944.

[14] ZHENG Z H，WANG P，LIU W，et al. Distance-IoU loss: faster and better learning for bounding box regression [J]. AAAI-20 Technical Tracks 7，2020，34(7): 12993-13000.

[15] HOU Q B，ZHOU D Q，FENG J S. Coordinate attention for efficient mobile network design [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition（CVPR）. Nashville: IEEE，2021: 13713-13722.

[16] HOWARD A G，ZHU M L，CHEN B，et al. MobileNets: efficient convolutional neural networks for mobile vision applications [R]. Los Alamos: arXiv Preprint，2017: arXiv: 1704.04861.

[17] YANG L X，ZHANG R Y，LI L D，et al. SimAM: a simple，parameter-free attention module for convolutional neural networks [C]//International Conference on Machine Learning. New York: PMLR，2021: 11863-11874.

[18] YANG X，YAN J C，MING Q，et al. Rethinking rotated object detection with Gaussian wasserstein distance loss [C]//International Conference on Machine Learning. New York: PMLR，2021: 11830-11841.

[19] JIANG Y Y，ZHU X Y，WANG X B，et al. R2CNN: rotational region CNN for orientation robust scene text detection [R]. Los Alamos: arXiv Preprint，2017: arXiv: 1706.09579.

[20] MA J Q，SHAO W Y，YE H，et al. Arbitrary-oriented scene text detection via rotation proposals [J]. IEEE Transactions on Multimedia,2018，20(11):3111-3122.

[21] YANG X，YAN J C，FENG Z M，et al. R3Det: refined single-stage detector with feature refinement for rotating object [J]. AAAI-21 Technical Tracks 4，2021，35(4): 3163-3171.

[22] LU D C，LI D M，LI Y L，et al. OSKDet: orientation-sensitive keypoint localization for rotated object detection [C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). New Orleans: IEEE，2022: 1182-1192.