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 | 296 | 37.19% |
General Classification | 23 | 2.89% |
Pedestrian Detection | 17 | 2.14% |
Autonomous Driving | 14 | 1.76% |
Classification | 13 | 1.63% |
Object Recognition | 11 | 1.38% |
Human Detection | 9 | 1.13% |
Few-Shot Object Detection | 9 | 1.13% |
Decoder | 8 | 1.01% |
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 |