What is the main difference between R-CNN and Fast R-CNN in object detection, and how does this difference improve performance?

The main difference between R-CNN (Regions with CNN features) and Fast R-CNN lies in where the Convolutional Neural Network (CNN) feature extraction is performed relative to the region proposals. In R-CNN, region proposals are first generated using a selective search algorithm. Then, each region proposal is independently warped to a fixed size and passed through the CNN to extract features. This process is computationally expensive because the CNN needs to be run separately for each region proposal. Fast R-CNN, on the other hand, performs feature extraction on the entire image using the CNN first. Then, region proposals are generated, and a Region of Interest (RoI) pooling layer is used to extract features from the feature map corresponding to each region proposal. This approach significantly reduces the computational cost because the CNN is only run once per image, rather than once per region proposal. For example, if an image has 2000 region proposals, R-CNN would run the CNN 2000 times, while Fast R-CNN would run it only once. This difference in the order of feature extraction leads to a significant improvement in performance, particularly in terms of speed. Fast R-CNN is much faster than R-CNN, allowing for near real-time object detection, whereas R-CNN is significantly slower due to the redundant CNN computations. Therefore, Fast R-CNN improves performance by performing feature extraction on the entire image first and then extracting features for each region proposal from the feature map, avoiding the redundant computations of R-CNN.