In Depth
Faster R-CNN (Region-based Convolutional Neural Network) is a two-stage object detection architecture introduced in 2015. The first stage uses a Region Proposal Network (RPN) to identify areas of the image that likely contain objects. The second stage extracts features from each proposed region and classifies them into specific object categories while refining the bounding box coordinates.
Faster R-CNN improved upon its predecessors (R-CNN and Fast R-CNN) by replacing external region proposal methods with a learned RPN that shares features with the detection network, making the entire pipeline trainable end-to-end. This sharing of convolutional features between proposal generation and classification made it much faster than earlier R-CNN variants.
While YOLO and other single-stage detectors are faster, Faster R-CNN's two-stage approach generally produces more accurate detections, especially for small objects and in dense scenes. It remains a preferred choice for applications where accuracy is more important than speed, such as medical image analysis, satellite imagery, and detailed scene understanding. Many modern detection architectures build on concepts first introduced in the R-CNN family.