二阶段网络

「爱情、让人受尽委屈。」 2023-01-06 01:56 164阅读 0赞

# 1、RCNN #

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对2000\*20的每一列分别进行极大值抑制，保留高质量建议框  
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# 2、fast-RCNN #

1、fast-RCNN的第二步于RCNN完全不同：  
RCNN将2000候选框送入CNN网络；fast-RCNN将整张图送入CNN网络得到特征图，将候选区域映射到特征图得到特征矩阵  
2、fast-RCNN的第三步于RCNN完全不同：  
RCNN专门训练SVM分类器和回归器  
fast-RCNN结合在一个网络中，不需要专门训练SVM分类器和回归器  
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每一个ROI区域划分为7\*7=49分做最大池化  
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L(cls):  
P(u):预测当前候选标签为u的概率  
(1)分类器损失  
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(1)边界框损失  
u>1:是正样本——>正样本计算才计算回归损失  
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Fast-RCNN瓶颈在于SS算法  
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# 3、Faster R-CNN #

Faster R-CNN=RPN+fast rcnn  
右边与Fast R-CNN一模一样；用RPN替代SS算法  
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右边相当于Fast R-CNN;左边RPN相当于替代了SS选取候选框。  
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分类：分为前景和背景

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**！！！！**  
anchor != 候选框  
利用RPN回归参数将anchor调整成候选框（proposal）  
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**原论文：多分类交叉熵损失,分类的K=2**  
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**二值交叉熵损失BCE 分类的k=1**  
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框架越来越简单并且简介效果越来越好  
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