[1] 聂光涛, 黄华.光学遥感图像目标检测算法综述[J].自动化学报, 2021, 47(8): 1749-1768.
NIE G T, HUANG H. A survey of object detection in optical remote sensing images[J]. Acta Automatica Sinica, 2021, 47(8): 1749-1768.
[2] 廖育荣, 王海宁, 林存宝, 等.基于深度学习的光学遥感图像目标检测研究进展[J].通信学报, 2022, 43(5): 190-203.
LIAO Y R, WANG H N, LIN C B, et al. Research progress of deep learning-based object detection of optical remote sensing images[J]. Journal on Communications, 2022, 43(5): 190-203.
[3] 张路青, 郭莹.基于卷积神经网络的遥感图像目标检测识别[J].舰船电子工程, 2023, 43(5): 49-53.
ZHANG L Q, GUO Y. Remote sensing image object detection and recognition based on convolutional neural network[J]. Ship Electronic Engineering, 2023, 43(5): 49-53.
[4] 赵加坤, 孙俊, 韩睿, 等.基于改进的Faster RCNN遥感图像目标检测[J].计算机应用与软件, 2022, 39(5): 192-196.
ZHAO J K, SUN J, HAN R, et al. Object detection based on improved Faster RCNN for remote sensing image[J]. Computer Applications and Software, 2022, 39(5): 192-196.
[5] 薛俊达, 朱家佳, 张静, 等.基于FFC-SSD模型的光学遥感图像目标检测[J].光学学报, 2022, 42(12): 138-148.
XUE J D, ZHU J J, ZHANG J, et al. Object detection in optical remote sensing images based on FFC-SSD model[J]. Acta Optica Sinica, 2022, 42(12): 138-148.
[6] 许新云. 光学遥感图像小目标检测方法研究[D]. 太原: 太原科技大学, 2023.
XU X Y. Research on small target detection methods in optical remote sensing images[D]. Taiyuan: Taiyuan University of Science and Technology, 2023.
[7] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]//Proceedings of the 15th European Conference on Computer Vision, 2018: 3-19.
[8] 余俊宇, 刘孙俊, 许桃.融合注意力机制的YOLOv7遥感小目标检测算法研究[J].计算机工程与应用, 2023, 59(20): 167-175.
YU J Y, LIU S J, XU T. Research on YOLOv7 remote sensing small target detection algorithm integrating attention mechanism[J]. Computer Engineering and Applications, 2023, 59(20): 167-175.
[9] PAN X, GE C, LU R, et al. On the integration of self-attention and convolution[C]//Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 815-825.
[10] 吴建成, 郭荣佐, 成嘉伟, 等. 注意力特征融合的快速遥感图像目标检测算法[J]. 计算机工程与应用, 2024, 60(1): 207-216.
WU J C, GUO R Z, CHENG J W, et al. A fast remote sensing image object detection algorithm based on attention feature fusion[J]. Computer Engineering and Applications, 2024, 60(1): 207-216.
[11] 梁礼明, 李仁杰, 董信, 等.基于上下文信息的遥感图像目标检测[J].电光与控制, 2023, 30(10): 89-94.
LIANG L M, LI R J, DONG X, et al. Target detection in remote sensing images based on context information[J]. Electronics Optics & Control, 2023, 30(10): 89-94.
[12] 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 detection algorithm for YOLOv7[J]. Computer Engineering and Applications, 2023, 59(15): 243-252.
[13] 李安达, 吴瑞明, 李旭东. 改进YOLOv7的小目标检测算法研究[J]. 计算机工程与应用, 2024, 60(1): 122-134.
LI A D, WU R M, LI X D. Research on improving YOLOv7’s small target detection algorithm[J]. Computer Engineering and Applications, 2024, 60(1): 122-134.
[14] 崔勇强, 黄谦, 高雪, 等.城市低空小型无人机目标实时高精度检测算法[J/OL].计算机工程与应用 [2023-11-22]. http://kns.cnki.net/kcms/detail/11.2127.TP.20230814.1642.
012.html.
CUI Y Q, HUANG Q, GAO X, et al. Real-time high-precision detection algorithm for small UAV targets in urban low-altitude areas[J/OL]. Computer Engineering and Applications [2023-11-22]. http://kns.cnki.net/kcms/detail/11.2127.TP.20230814.1642.012.html.
[15] QI Z T, REN Y, LONG J, et al. Application of YOLOv7 in remote sensing image target detection[C]//Proceedings of the 42nd Chinese Control Conference, Tianjin, Jul 24-26, 2023: 7603-7608.
[16] 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[J]. arXiv:2207.02696, 2022.
[17] DAI J F, QI H Z, XIONG Y W, et al. Deformable convolutional networks[J]. arXiv:1703.06211, 2017.
[18] WANG W H, DAI J F, CHEN Z, et al. InternImage: exploring large-scale vision foundation models with deformable convolutions[C]//Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, 2023: 14408-14419.
[19] VASWANI A, SHAZZER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st Conference on Neural Information Processing Systems, Long Beach, 2017: 5998-6008.
[20] ZHU L, WANG X J, KE Z H, et al. BiFormer: vision transformer with bi-level routing attention[C]//Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, 2023: 10323-10333.
[21] ZHENG Z H, WANG P, REN D W, et al. Enhancing geometric factors in model learning and inference for object detection and instance segmentation[J]. IEEE Transactions on Cybernetics, 2021, 52(8): 8574-8586.
[22] ZHENG Z H, WANG P, LIU W, et al. Distance-IoU loss: faster and better learning for bounding box regression[J]. arXiv:1911.08287, 2019.
[23] MA S L, XU Y. MPDIoU: a loss for effcient and accurate bounding box regression[J]. arXiv:2307.07662, 2023.
[24] XIA G S, BAI X, DING J, et al. DOTA: a large-scale dataset for object detection in aerial images[C]//Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018: 3974-3983.
[25] 张洋, 夏英.多尺度特征融合的遥感图像目标检测方法[J/OL]. 计算机科学[2023-10-05]. http://kns.cnki.net/kcms/detail/50.1075.tp.20230925.1342.094.html.
ZHANG Y, XIA Y. Object detection method with multi-scale feature fusion for remote sensing images[J/OL]. Computer Science [2023-10-05]. http://kns.cnki.net/kcms/detail/50.1075.tp.20230925.1342.094.html.
[26] HU J M, ZHI X Y, SHI T J, et al. PAG-YOLO: a portable attention-guided YOLO network for small ship detection[J]. Remote Sensing, 2021, 13(16): 3059.
[27] ZHANG J Q, LEI J, XIE W Y, et al. SuperYOLO: super resolution assisted object detection in multimodal remote sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 1-15. |