FasterRCNN

Faster R-CNN is a convolutional neural network-based object detection algorithm. It is an extension of the Region-based Convolutional Neural Network (R-CNN) family, aiming to improve both speed and accuracy. The core innovation of Faster R-CNN is the introduction of a Region Proposal Network (RPN). This RPN is a fully convolutional network that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. The RPN predicts object bounds and objectness scores at each position in an image. This significantly speeds up the process compared to earlier methods that relied on external region proposal algorithms like Selective Search.

The overall architecture of Faster R-CNN consists of three main components. First, a backbone network (often a deep convolutional network like VGG or ResNet) extracts features from the input image. Second, the Region Proposal Network takes these features and generates a set of candidate object regions, known as region proposals. Finally, these proposals are fed into a detection network, which refines the bounding boxes and classifies the objects within each proposal. This integrated approach allows for end-to-end training, making the entire system more efficient and effective for object detection tasks. Faster R-CNN has been a foundational model in object detection and has influenced subsequent research in the field.