Object Detection Develop History-1

Two Stage Method
stage 1 = region proposal
stage 2 = feature extract + bounding box

R-CNN
Fast R-CNN
Faster R-CNN
R-FCN

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R-CNN
用傳統的computer vison 的selective search找region proposal
但是stage 2用CNN找FEATURE + BOUNDING BOX + SVM辨識目標種類

缺點: 太慢

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Fast R-CNN

改進: 每個region proposal共用CNN來找feature&SVM換成Neural Network來加速

缺點: 後面分類物體 + 給bounding box變快，前面的region proposal就顯得很慢

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Faster R-CNN

改進: region proposal也用CNN來加速

缺點: 每個proposal區域的分類還是要單獨算一次

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R-FCN

改進: 整張影像的分類只要算一次

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Two Stage Method Pros and Cons

Accuracy: High
Speed: Slow(Relatively)

因此產生了不再分兩個階段的method –-> One Stage Method

One Stage Method

Accuracy: Low
Speed: fast(Relatively)
ex. YOLO
將兩個階段的CNN合併成一個網路，同時選取proposal + classification

但是，還有一種加速two stage的method–-> Light weighted

Light weighted Two Stage

ex. PVANet

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Summary

YOLO已經有5代了

tags: SAR Deep learning Object Detection review