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Remote Sensing Image Object Detection Method Based On Improved YOLOv3


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Zou Z, Shi Z, Guo Y, et al. Object detection in 20 years: A survey[J]. arXiv preprint arXiv:1905.05055, 2019. ZouZ ShiZ GuoY Object detection in 20 years: A survey[J] arXiv preprint arXiv:1905.05055 2019 Search in Google Scholar

Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]//2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05). Ieee, 2005, 1: 886–893. DalalN TriggsB Histograms of oriented gradients for human detection[C] 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05) Ieee 2005, 1 886 893 Search in Google Scholar

Felzenszwalb P, McAllester D, Ramanan D. A discriminatively trained, multiscale, deformable part model[C]//2008 IEEE conference on computer vision and pattern recognition. Ieee, 2008: 1–8. FelzenszwalbP McAllesterD RamananD A discriminatively trained, multiscale, deformable part model[C] 2008 IEEE conference on computer vision and pattern recognition Ieee 2008 1 8 Search in Google Scholar

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. 2014: 580–587. GirshickR DonahueJ DarrellT Rich feature hierarchies for accurate object detection and semantic segmentation[C] Proceedings of the IEEE conference on computer vision and pattern recognition 2014 580 587 Search in Google Scholar

Girshick R. Fast r-cnn[C]//Proceedings of the IEEE international conference on computer vision. 2015: 1440–1448. GirshickR Fast r-cnn[C] Proceedings of the IEEE international conference on computer vision 2015 1440 1448 Search in Google Scholar

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. RenS HeK GirshickR Faster r-cnn: Towards real-time object detection with region proposal networks[J] Advances in neural information processing systems 2015 28 Search in Google Scholar

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. RedmonJ DivvalaS GirshickR You only look once: Unified, real-time object detection[C] Proceedings of the IEEE conference on computer vision and pattern recognition 2016 779 788 Search in Google Scholar

Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 7263–7271. RedmonJ FarhadiA YOLO9000: better, faster, stronger[C] Proceedings of the IEEE conference on computer vision and pattern recognition 2017 7263 7271 Search in Google Scholar

Redmon J, Farhadi A. Yolov3: An incremental improvement[J]. arXiv preprint arXiv:1804.02767, 2018. RedmonJ FarhadiA Yolov3: An incremental improvement[J] arXiv preprint arXiv:1804.02767 2018 Search in Google Scholar

Liu W, Anguelov D, Erhan D, et al. Ssd: Single shot multibox detector[C]//European conference on computer vision. Springer, Cham, 2016: 21–37. LiuW AnguelovD ErhanD Ssd: Single shot multibox detector[C] European conference on computer vision Springer, Cham 2016 21 37 Search in Google Scholar

Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE international conference on computer vision. 2017: 2980–2988. LinT Y GoyalP GirshickR Focal loss for dense object detection[C] Proceedings of the IEEE international conference on computer vision 2017 2980 2988 Search in Google Scholar

Tuia D, Persello C, Bruzzone L. Domain adaptation for the classification of remote sensing data: An overview of recent advances[J]. IEEE geoscience and remote sensing magazine, 2016, 4(2): 41–57. TuiaD PerselloC BruzzoneL Domain adaptation for the classification of remote sensing data: An overview of recent advances[J] IEEE geoscience and remote sensing magazine 2016 4 2 41 57 Search in Google Scholar

Li K, Wan G, Cheng G, et al. Object detection in optical remote sensing images: A survey and a new benchmark[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 159: 296–307. LiK WanG ChengG Object detection in optical remote sensing images: A survey and a new benchmark[J] ISPRS Journal of Photogrammetry and Remote Sensing 2020 159 296 307 Search in Google Scholar

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. LiuS QiL QinH Path aggregation network for instance segmentation[C] Proceedings of the IEEE conference on computer vision and pattern recognition 2018 8759 8768 Search in Google Scholar

Ge Z, Liu S, Li Z, et al. Ota: Optimal transport assignment for object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 303–312. GeZ LiuS LiZ Ota: Optimal transport assignment for object detection[C] Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2021 303 312 Search in Google Scholar

Lin T Y, Dollár 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. LinT Y DollárP GirshickR Feature pyramid networks for object detection[C] Proceedings of the IEEE conference on computer vision and pattern recognition 2017 2117 2125 Search in Google Scholar

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