---
# System prepended metadata

title: Object Detection Develop History-1
tags: [SAR, Object Detection, Deep learning, review]

---

# 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
![](https://i.imgur.com/0ghFz57.png)

缺點: 太慢

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==Fast R-CNN==
:::info
改進: 每個region proposal共用CNN來找feature&SVM換成Neural Network來加速
:::
![](https://i.imgur.com/sC9bl3n.png)
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==
:::info
改進: region proposal也用CNN來加速
:::
![](https://i.imgur.com/MxVyxjy.png)
缺點: 每個proposal區域的分類還是要單獨算一次

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==R-FCN==
:::info
改進: 整張影像的分類只要算一次
:::
![](https://i.imgur.com/QJ1aAf9.png)

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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
===
![](https://i.imgur.com/U9ZYUEw.png)
ex. PVANet

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Summary
===
![](https://i.imgur.com/q3cqyob.png)
:::warning
YOLO已經有5代了
:::

###### tags: `SAR` `Deep learning` `Object Detection` `review` 
