文献紹介：A Survey of Deep Learning-Based Object Detection
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Licheng Jiao, Fan Zhang, Fang Liu, Shuyuan Yang, Lingling Li, Zhixi Feng, Rong Qu, "A Survey of Deep Learning-Based Object Detection", IEEE Access, Vol.7, pp. 128837-128868, 2019. https://ieeexplore.ieee.org/document/8825470
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文献紹介：A Survey of Deep Learning-Based Object Detection
- 1. A Survey of Deep Learning-Based Object Detection Jiao, Licheng and Zhang, Fan and Liu, Fang and Yang, Shuyuan and Li, Lingling and Feng, Zhixi and Qu, Rong IEEE Access, 2019 , 2022/06/17
- 2. ◼ ◼ • two-stage • one-stage • 2019 ◼ ◼ ◼ ◼
- 5. two-stage one-stage ◼two-stage • Faster R-CNN [Ren+, NeurIPS2015] ◼one-stage ◼one-stage • YOLO [Redmon+, CVPR2016] • SSD [Liu+, ECCV2016] ◼two-stage BBox two-stage one-stage end-to-end
- 6. two-stage
- 7. R-CNN Fast R-CNN ◼R-CNN [Girshick+, CVPR2014] • CNN • SVM • • CNN • ◼Fast R-CNN [Girshick, ICCV2015] • • RoI region of interest pooling • region proposal
- 8. R-CNN ◼Faster R-CNN [Ren+, NeurIPS2015] • RPN region proposal network multi-scale anchors Fast R-CNN • ◼Mask R-CNN [He+, ICCV2017] • ResNet [He+, CVPR2016] -FPN [Lin+, CVPR2017] • RoI pooling RoIAlign • 1 ◼Cascade R-CNN [Cai and Vasconcelos, CVPR2018] • IoU RoIAlign
- 9. one-stage
- 10. SSD Single Shot Detection ◼ DBox • BBox NMS Localization, confidence 38 38 19 19 19 19 10 10 5 5 3 3 1 1 Non- maximum suppression conv conv conv conv conv conv 300 300 [Liu+, ECCV2016]
- 11. NMS Non-maximum Suppression ◼BBox • confidence score BBox • BBox IoU confidence score BBox Non- maximum suppression BBox [Liu+, ECCV2016] 𝐼𝑜𝑈 = 𝐴𝑟𝑒𝑎 𝑜𝑓 𝑂𝑣𝑒𝑟𝑙𝑎𝑝 𝐴𝑟𝑒𝑎 𝑜𝑓 𝑈𝑛𝑖𝑜𝑛
- 12. one-stage ◼Feature Pyramid Networks • RetinaNet [Lin+, ICCV2017] • Focal Loss • M2Det [Zhao+, AAAI2018] • Multi-Level FPN ◼RefineDet [Zhang+, CVPR2018] • one-stage two-stage RetinaNet RefineDet
- 13. 2019
- 14. Relatioal Networks [Hu+, CVPR2018] ◼SSD NMS BBox • ◼object relation module • • • end to end BBox object relation module
- 15. DCNv2 [Zhu+, CVPR2019] ◼DCN [Dai+, ICCV2017] • receptive field ◼Modulated deformable convolution • Modulation deformable RoI pooling standard convolution deformable convolution 3 3
- 16. NAS-FPN [Ghiasi+, CVPR2019] ◼NAS Neural Architecture Search FPN • RNN Controller (b)-(f) NAS-FPN / Proxy task AP
- 18. 2 one-stage
- 20. ◼PASCAL VOC ◼COCO • COCO mAP ◼ImageNet ◼VisDrone 2018 ◼Open Images ◼Pedestrian detection datasets • Caltech • KITTI • CityPersons • TDC • EuroCity Persons
- 21. AP mAP COCO mAP ◼Precision Recall IoU 0.5 • Precision = BBox(IoU≥0.5) BBox (all) • Recall = BBox(IoU≥0.5) Gt BBox (all) ◼AP Average Precision • AP = 0 1 p r dr • Recall vs Precision AP • ◼mAP • AP • COCO IoU = [0.5, 0.55, … , 0.95] mAP BBox / BBox BBox / BBox
- 23. ◼FPN • MASK R-CNN, NAS-FPN, FCOS [Tian+, ICCV2019] ◼SSD • WeaveNet [Chen+, arXiv2017] ESSD [Zheng+, arXiv2018] ◼ • RefineDet, R-DAD [Bae, AAAI2019] ◼ • Attention mechanism [Zhang & Kim, CVPR2019] • SSD [Kong+, ECCV2018] ◼ • DCN DCNv2 15
- 24. loss ◼IoU loss • Unit Box [Yu+, ACM MM 2016] ◼ BBox regression loss • BBox [He+, CVPR2019] • Softer-NMS [He+, arXiv2019] ◼ • Axially Localized Detection [Cabriel+, nature communicaitions2019] ◼one-stage • Hard negative mining [Bucher+, arXiv2016] ◼ Hard mining • IoU-balanced sampling [Pang+, CVPR2019] ◼loss • RetinaNet • AP-loss [Chen+, CVPR2019]
- 25. NMS ◼NMS • Relation Networks 14 ◼ BBox Gt BBox IoU • IoU-Net learning [Jiang+, ECCV2018] ◼IoU Confidence score • Fitness NMS [Tychsen-Smith & Petersson, CVPR2018] ◼NMS • Softer-NMS [He+, arXiv2019]
- 26. 1 ◼ • ◼SSD • [Jeong+, arXiv2017] • Context-Aware SSD [Xiang+, arXiv2018] ◼GAN [Goodfellow, NeurIPS2014] • Perceptual GAN [Li+, CVPR2017] ◼ ◼ • Face Attention Network [Wang+, arXiv2017] ◼ • Reputation loss [Wang+, IEEE Access 2018] • Occlusion-aware R-CNN [Zhang+, ECCV2018]
- 27. 2 ◼ • • • anchor BBox Faster R-CNN SSD
- 28. anchor-free ◼anchor • anchor • anchor • ◼anchor-free • CornerNet [Law and Deng, ECCV2018] • FCOS [Tian+, ICCV2019] • • CenterNet [Duan+, ICCV2019]
- 29. ◼ • YOLO YOLO9000 [Redmon & Farhadi, CVPR2017] • WeaveNet [Chen+, arXiv2017] ESSD [Zheng+, arXiv2018] • Pelee [Wang+, NeurIPS2018] ◼ • RetinaNet 12 • RFBNet [Liu+, ECCV2018] • pRF RFBNet RFB module
- 30. ◼ • ScrachDet [Zhu+, CVPR2019] • ◼ • DetNet [Li+, ECCV2018] • • Light-Head R-CNN [Li+, arXiv2017] • two-stage
- 34. ◼ • ISPRS dataset [Audebert+, MDPI 2017] True positive false positive Grand truth
- 36. ◼ [Li+, arXiv2019] 1 2 3 CAM[Zhou+, arXiv2015] 4 ablation study
- 37. ◼ 1 3 RetinaNet 2 3 SKU-110K [Goldman+, CVPR2019] RetinaNet