Faster R-CNN

참고: Selective Search(https://donghwa-kim.github.io/SelectiveSearch.html) 참고: EdgeBoxes(https://donghwa-kim.github.io/EdgeBoxes.html) 참고: https://www.youtube.com/watch?v=nDPWywWRIRo

1. Abstract
   • SPPNet [1] and Fast R-CNN [2] have reduced the running time of detection networks, exposing region proposal computation as a bottleneck
   • We introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network (predicts object bounds and objectness scores at each position)
   • Use Fast R-CNN for detection, changed Region Proposal Algorithm into RPN merging RPN and Fast R-CNN into a single network (Only 300 proposals; 2000 proposals in R-CNN)

2. Introduction
   • Now, proposals are the test-time computational bottleneck in state-of-the-art detection systems
     • Selective Search [4], one of the most popular methods, greedily merges superpixels based on engineered low-level features
     • EdgeBoxes [6] currently provides the best tradeoff between proposal quality and speed
   • We introduce novel Region Proposal Networks that share convolutional layers with state-of-the-art object detection networks
   • Our observation is that the convolutional feature maps used by region-based detectors, like Fast RCNN, can also be used for generating region proposals

   

   • RPNs are designed to efficiently predict region proposals with a wide range of scales and aspect ratios (Figure 1-a, 1-b, 1-c)
   • RPNs completely learn to propose regions from data, and thus can easily benefit from deeper and more expressive features

3. Related Work
   • Object Proposals
     • Grouping super-pixels: Selective Search [4], CPMC [22], MCG [23]
     • Sliding windows: objectness in windows [24], EdgeBoxes [6]
   • Deep Networks for Object Detection
     • R-CNN [5]
     • Predicting object bounding boxes: [25], OverFeat method [9], MultiBox methods [26, 27], DeepMask method [28]
     • Shared Computation of convolutions: [9, 1, 29, 7, 2]

4. Faster R-CNN

   

   • Region Proposal Networks 참고: ZFNet(https://oi.readthedocs.io/en/latest/computer_vision/cnn/zfnet.html)
     • Network takes as input an $n * n$ spatial window
     • Intermediate layer maps input into a lower dimensional feature
     • Box-regression layer and a box-classification layer are used
     • Anchor is centered at the sliding window in question, and is associated with a scale and aspect (by default 3 scales([128, 256, 512]) * 3 aspect ratios([1:1, 1:2, 2:1]), total k=9 anchors at each sliding position)
     • Translation-Invariant Anchors: Anchors and the Functions that compute proposals relative to the anchors
     • Multi-Scale Anchors as Regression References
     • Loss Function (Multi-task Loss)

       

     • dd

5. Experiments

6. Conclusion

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