---
# System prepended metadata

title: Detection of Emotions

---

# Detection of Emotions
## Detection(物件偵測)
**1.one-stage learning**
定義:
物件偵測辨識一步到位
![](https://i.imgur.com/gk8rpJt.png)

==ex:Google在2015年12月提出Single Shot Detector (SSD)，Google在文章摘要第一句話就寫「We present a method for detecting objects in images using a single deep neural network.」，一個深度神經網路就可以做完所有的物件偵測。==
缺點:
辨識精度(precision)低(**但還可接受**)
優點:
速度快
結論:
行動裝置仍大部分使用one-stage 
例子:
YOLO(YOU ONLY LOOK ONCE)->YOLOv1 v2 v3 ,Tiny YOLO
Single Shot Detector(SSD)
RetinaNet
EfficientDet


**2.two-stage learning**
定義:
分 **(1)選出物件** **(2)物件辨識** 
**First step**
選出物件(Region Proposal)
**Second step**
物件辨識(Recognition)-->(1)物件分類(classification only)(2)特徵擷取(feature extraction)加分類(classification)
![](https://i.imgur.com/2UOH2o4.png)
缺點:
耗時
若一張圖內有非常多物件需要擷取分類,一個物件假設需0.1sec擷取,除非有很強的gpu,不然還是無法即時運算,所以需要one-stage
![](https://i.imgur.com/9p1hS7w.jpg)
優點:
精確度較高
例子:![](https://i.imgur.com/EikeGo9.png)
R-CNN->fast R-CNN->faster R-CNN
![](https://i.imgur.com/gpTBbtJ.png)
High-level architecture of R-CNN (top) and Fast R-CNN (bottom) object detection.



