1）R(Regions)-CNN https://arxiv.org/abs/1311.2524
Girshick R, Donahue J, Darrell T, et al. Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation[C]// Computer Vision and Pattern Recognition. IEEE, 2013:580-587.
2）Fast R-CNN https://arxiv.org/abs/1504.08083
Girshick R. Fast R-CNN[C]// IEEE International Conference on Computer Vision. IEEE, 2015:1440-1448.
3）Faster R-CNN https://arxiv.org/abs/1506.01497
Girshick R. Fast r-cnn[C]//Proceedings of the IEEE International Conference on Computer Vision. 2015: 1440-1448.
4）YOLO https://arxiv.org/abs/1506.02640
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）SSD https://arxiv.org/abs/1512.02325
Liu W, Anguelov D, Erhan D, et al. SSD: Single shot multibox detector[C]//European Conference on Computer Vision. Springer International Publishing, 2016: 21-37.
6）YOLOv2 https://arxiv.org/abs/1612.08242
Redmon J, Farhadi A. YOLO9000: Better, Faster, Stronger[J]. arXiv preprint arXiv:1612.08242, 2016.
7）Mask R-CNN https://arxiv.org/abs/1703.06870
He K, Gkioxari G, Dollár P, et al. Mask R-CNN[J]. arXiv preprint arXiv:1703.06870, 2017.
R-CNN: region proposal可能是object的區(qū)域?放到CNN獲得分類钳恕；問題：區(qū)域重疊多卸察，重復(fù)計(jì)算量大
Fast R-CNN: 多個(gè)可能的區(qū)域一起放到CNN，只做一次feature提取，獲得分類；問題：獲得region的速度太慢
Faster R-CNN: region proposal放到CNN里來完成，CNN?CNN讼积；問題：獲得位置?分類，兩步走速度慢
YOLO：位置+分類?回歸問題脚仔，一步解決勤众；問題：小物體、挨得很近的物體鲤脏、尺寸不太常見的物體的漏檢
SSD：借鑒Faster R-CNN和YOLO们颜，增加幾個(gè)對(duì)不同尺寸的感知層
YOLO2：調(diào)整特征提取部分的網(wǎng)絡(luò)結(jié)構(gòu)?更快，使用歸一化猎醇、針對(duì)細(xì)顆粒度窥突、高分辨率進(jìn)行優(yōu)化，修改訓(xùn)練策略?更準(zhǔn)
