Object Detection Models

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 Networks

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Object Detection 285 36.87%
Test 25 3.23%
General Classification 23 2.98%
Pedestrian Detection 16 2.07%
Autonomous Driving 14 1.81%
Classification 13 1.68%
Object Recognition 11 1.42%
Few-Shot Object Detection 9 1.16%
Pose Estimation 8 1.03%

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