Faster R-CNN is an object detection model that improves on Fast R-CNN by utilising a region proposal network (RPN) with the CNN model. The RPN shares full-image convolutional features with the detection network, enabling nearly cost-free region proposals. It is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. RPN and Fast R-CNN are merged into a single network by sharing their convolutional features: the RPN component tells the unified network where to look.
As a whole, Faster R-CNN consists of two modules. The first module is a deep fully convolutional network that proposes regions, and the second module is the Fast R-CNN detector that uses the proposed regions.
Source: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal NetworksPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Object Detection | 316 | 29.42% |
Object | 176 | 16.39% |
General Classification | 23 | 2.14% |
Deep Learning | 20 | 1.86% |
Pedestrian Detection | 17 | 1.58% |
Classification | 14 | 1.30% |
Autonomous Driving | 14 | 1.30% |
Object Recognition | 12 | 1.12% |
2D Object Detection | 9 | 0.84% |
Component | Type |
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Convolution
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Convolutions | |
RoIPool
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RoI Feature Extractors | |
RPN
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Region Proposal | |
Softmax
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Output Functions |