Laser Journal, Volume. 45, Issue 12, 81(2024)
Remote sensing small target detection integrating receptive field amplification and feature enhancement
[5] [5] Girshick R, Donahue J, Darrell T, et al. Richfeature hierarchies for accurate object detecti-on and semantic segmentation[C]//Proceedin-gs of the IEEE conference on computer visi-on and pattern recognition, 2014: 580-587.
[6] [6] Girshick R. Fast r-cnn[C]//Proceedings of th-e IEEE international conference on computer vision, 2015: 1440-1448.
[7] [7] Ren S, He K, Girshick R, et al. Faster r-cn-n: Towards real-time object detection with reg-ion proposal networks[J]. Advances in neuralinformation processing systems, 2015, 28.
[8] [8] He K, Gkioxari G, Dollr P, et al. Mask r-c-nn[C]//Proceedings of the IEEE internationalconference on computer vision, 2017: 2961-2969.
[9] [9] Redmon J, Divvala S, Girshick R, et al. Youonly look once: Unified, real-time object dete-ction[C]//Proceedings of the IEEE conferenceon computer vision and pattern recognition, 2016: 779-788.
[10] [10] Redmon J, Farhadi A. YOLO9000: better, fa-ster, stronger[C]//Proceedings of the IEEE c-onference on computer vision and pattern re-cognition, 2017: 7263-7271.
[11] [11] Redmon J, Farhadi A. YOLOv3: an increme-ntal improvement[EB/OL](2018-04-08)[2023-07-01].
[12] [12] Bochkovskiy A, Wang C, Liao H. Yolov4: op-timal speed and accuracy of object detection[EB/OL](2020-04-23)[2023-07-01].
[13] [13] Ge Z, Liu S, Wang F, et al. YOLOX: exc-eeding YOLO series in 2021[C]//Proceeding-s of the IEEE Conference on Computer Visi-on and Pattern Recognition, 2021: 1-7.
[14] [14] 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.
[15] [15] Liu W, Anguelov D, Erhan D, et al. SsD: Si-ngle shot multibox detector[C]//European C-onference on Computer Vision, 2016: 21-37.
[16] [16] Duan K, Bai S, Xie L, et al. Centernet: Keypoint triplets for object detection[C]//Procee-dings of the IEEE/CVF international confere-nceon computer vision, 2019: 6569-6578.
[20] [20] Liu Z, Lin Y, Cao Y, et al. Swin transforme-r: Hierarchical vision transformer using shifte-d windows[C]//Proceedings of the IEEE/CVFinternational conference on computer vision, 2021: 10012-10022.
[26] [26] Shi T, Gong J, Hu J, et al. Feature-enhancedcenternet for small object detection in remotesensing images[J]. Remote Sensing, 2022, 14(21): 5488.
[27] [27] Zhu L, Wang X, Ke Z, et al. BiFormer: Vis-ion Transformer with Bi-Level Routing Atten-tion[C]//Proceedings of the IEEE/CVF Conf-erence on Computer Vision and Pattern Reco-gnition, 2023: 10323-10333.
[28] [28] Zhang H, Wang Y, Dayoub F, et al. Varifocalnet: An iou-aware dense object detector[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021: 8514-8523.
[29] [29] Zheng Z, Wang P, Liu W, et al. Distance-Io-U loss: Faster and better learning for boundi-ng box regression[C]//Proceedings of the A-AAI conference on artificial intelligence, 2020, 34(7): 12993-13000.
[30] [30] Li X, Wang W, Wu L, et al. Generalized fo-cal loss: Learning qualified and distributed b-ounding boxes for dense object detection[J]. Advances in Neural Information Processing Systems, 2020, 33: 21002-21012.
[31] [31] Hou Q, Zhou D, Feng J. Coordinate attentionfor efficient mobile network design[C]//Proc-eedings of the IEEE/CVF conference on co-mputer vision and pattern recognition, 2021: 13713-13722.
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TIE Jun, QIN Jintian, ZHENG Lu, ZHENG Mingxue, CHEN Ting. Remote sensing small target detection integrating receptive field amplification and feature enhancement[J]. Laser Journal, 2024, 45(12): 81
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Received: Mar. 4, 2024
Accepted: Mar. 10, 2025
Published Online: Mar. 10, 2025
The Author Email: Lu ZHENG (lu2008@mail.scuec.edu.cn)