Feature Pyramid Networks for Object Detection​

1. FPN : Feature Pyramid Networks for Object Detection
Deep Learning Study
Hansol Kang

2. Contents
2019-07-22
2
Introduction
FPN
Summary

3. Introduction
Kaiming He
2019-07-22
3

4. Introduction
Kaiming He
2019-07-22
4
Kaiming He... 당신은 대체...
ResNet
Faster R-CNN
DCP
SRCNN
SPPNet
Mask R-CNN
R-FCN
FPN
RetinaNet

5. Introduction
Image Pyramid
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6. FPN(Feature Pyramid Networks)
FCN(Fully Convolutional Networks)란?
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Input
image
Output
Conv
Conv
FC

7. Introduction
FCN(Fully Convolutional Networks)란?
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FC의 가장 큰 문제점
: 지역적인 위치 정보를 잃어버림.
2차원 정보를 1차원으로 해석하기 때문.
FCN의 등장
: 지역적인 위치 정보를 보존함.
2차원 정보를 그대로 해석함.
GoogLeNet과 유사하게 1x1 convolution을 활용함.

8. Introduction
FCN(Fully Convolutional Networks)란?
2019-07-22
8
Input
image
Conv
Conv
• 2D를 그대로 가져감.
• 1x1 conv를 이용하여 depth만 줄임.
• FC가 없어져서 input size를 고려할 필요 없음.
• Classification을 예로 들면, depth는 class에 해당함.
(<->FC에서는 노드 개수가 class)
Output

9. Introduction
Classification Vs. Detection
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Classification Detection
Spiderman : 83%
Superman : 15%
Deadpool : 2%
ㅁ : Spiderman 83%
ㅁ : Spiderman 12%
ㅁ : Spiderman 12%
Classification
Translation Invariance
Detection
Translation Variance

10. R-FCN
Position-Sensitive Score Map
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Translation Variance 성질을 부여함.

11. R-FCN
Position-Sensitive RoI Pooling
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12. R-FCN
Position-Sensitive RoI Pooling
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13. R-FCN
Position-Sensitive RoI Pooling
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14. R-FCN
Position-Sensitive RoI Pooling
2019-07-22
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15. R-FCN
Position-Sensitive RoI Pooling
2019-07-22
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16. R-FCN
Training
2019-07-22
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*
t : Ground-truth box
reg
L : bounding box regression loss
>0.5 : positive
o.t : negative
GT
Predict
Softmax
score map
Regression
loss(IoU)

17. R-FCN
Results
2019-07-22
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18. Summary
정리
2019-07-22
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• 기존의 R-CNN 계열에 FCN을 접목한 detection network에 대해 학습함.
• 해당 R-FCN 논문에서는 FCN에 position-sensitive score map을 접목하여 translation varianc하게 네트
워크를 제안함.
• Position-sensitive RoI polling 으로부터 position-sensitive score map을 구하고, 이를 voting을 통하여
최종적인 결과값을 얻음.

19. &
2019-07-22 19