Laser Journal, Volume. 45, Issue 12, 49(2024)
Improved YOLOv5s Algorithm for rotation object detection in remote sensing images
[1] [1] Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks[J]. Advances in neural information processing systems, 2015, 28: 2176-2184.
[2] [2] Cai Z, Vasconcelos N. Cascade r-cnn: Delving into high quality object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 6154-6162.
[3] [3] 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.
[4] [4] 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.
[5] [5] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 7263-7271.
[6] [6] Redmon J, Farhadi A. Yolov3: An incremental improvement[J]. arXiv preprint arXiv: 1804.02767, 2018.
[7] [7] Bochkovskiy A, Wang C Y, Liao H Y M. Yolov4: Optimal speed and accuracy of object detection[J]. arXiv preprint arXiv: 2004.10934, 2020.
[8] [8] Li C, Li L, Jiang H, et al. YOLOv6: A single-stage object detection framework for industrial applications[J]. arXiv preprint arXiv: 2209.02976, 2022.
[9] [9] Wang C Y, Bochkovskiy A, Liao H Y M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for realtime object detectors[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023: 7464-7475.
[10] [10] Ma J, Shao W, Ye H, et al. Arbitrary-oriented scene text detection via rotation proposals[J]. IEEE transactions on multimedia, 2018, 20(11): 3111-3122.
[11] [11] Jiang Y, Zhu X, Wang X, et al. R2CNN: Rotational region CNN for orientation robust scene text detection[J]. arXiv preprint arXiv: 1706.09579, 2017.
[12] [12] Ding J, Xue N, Long Y, et al. Learning RoI transformer for oriented object detection in aerial images[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019: 2849-2858.
[14] [14] Yang X, Yang X, Yang J, et al. Learning high-precision bounding box for rotated object detection via kullback-leibler divergence[J]. Advances in Neural Information Processing Systems, 2021, 34: 18381-18394.
[15] [15] Li W, Chen Y, Hu K, et al. Oriented reppoints for aerial object detection[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022: 1829-1838.
[18] [18] Yang X, Yan J. Arbitrary-oriented object detection with circular smooth label[C]//Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28,2020, Proceedings, Part VIII 16. Springer International Publishing, 2020: 677-694.
[19] [19] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, June 18-22,2018: 7132-7141.
[20] [20] Woo S, Park J, Lee J Y, et al. Cbam: Convolutional block attention module[C]//Proceedings of the European conference on computer vision (ECCV), Munich, Germany, Sep 8-14,2018: 3-19.
[21] [21] Hou Q, Zhou D, Feng J. Coordinate attention for efficient mobile network design[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 13713-13722.
[22] [22] Wang Q, Wu B, Zhu P, et al. ECA-Net: Efficient channel attention for deep convolutional neural networks[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 11534-11542.
[23] [23] Lin T Y, Dollr P, Girshick R, et al. Feature pyramid networks for object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 2117-2125.
[24] [24] Liu S, Qi L, Qin H, et al. Path aggregation network for instance segmentation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 8759-8768.
[25] [25] Tan M, Pang R, Le Q V. Efficientdet: Scalable and efficient object detection[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 10781-10790.
[26] [26] Liu S, Huang D, Wang Y. Learning spatial fusion for single-shot object detection[J]. arXiv preprint arXiv: 1911.09516, 2019.
[27] [27] Xia G S, Bai X, Ding J, et al. DOTA: A large-scale dataset for object detection in aerial images[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 3974-3983.
[28] [28] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30: 1745-1750.
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LIU Bingbing, HU Yaoguo, YAN Peng, ZHANG Qinlin. Improved YOLOv5s Algorithm for rotation object detection in remote sensing images[J]. Laser Journal, 2024, 45(12): 49
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Received: Mar. 29, 2024
Accepted: Mar. 10, 2025
Published Online: Mar. 10, 2025
The Author Email: Qinlin ZHANG (zhangqinglin@mail.ccnu.edu.cn)