Fast R-CNN is an advanced object detection algorithm that improves upon its predecessor, R-CNN. It streamlines the process by using a single convolutional neural network (CNN) to extract features from an entire image, rather than processing each region proposal separately. This significantly speeds up the detection process while maintaining high accuracy. The algorithm operates in two main stages: first, it generates region proposals using a method like Selective Search, and then it classifies these proposals and refines their bounding boxes using the CNN. This efficient approach allows Fast R-CNN to achieve faster training and testing times compared to earlier models.