Faster R-CNN: Object Detection with Speed and Accuracy

Faster R-CNN is a deep learning model for object detection that was proposed by Shaoqing Ren et al. in 2015. The model is an extension of the popular R-CNN (Region-based Convolutional Neural Network) model that uses a region proposal network (RPN) to generate object proposals instead of using selective search. This makes the model significantly faster and more accurate than its predecessor.

Faster R-CNN consists of two main components: the RPN and the Fast R-CNN detector. The RPN generates object proposals by sliding a small network over the convolutional feature map and predicting objectness scores and bounding box offsets at each location. The Fast R-CNN detector then takes these proposals and extracts features from them to classify and localize the objects.

Faster R-CNN has become a popular choice for object detection tasks due to its speed and accuracy, and has been used in various applications such as autonomous driving, surveillance, and medical imaging.