R-CNN, or Region-based Convolutional Neural Network, is a deep learning model designed for object detection in images. It works by first generating potential bounding boxes around objects in an image using a method called selective search. These regions are then fed into a convolutional neural network to classify the objects and refine the bounding boxes. The R-CNN approach significantly improves the accuracy of object detection compared to previous methods. It combines the strengths of traditional computer vision techniques with deep learning, allowing for better feature extraction and recognition. This model laid the groundwork for further advancements in object detection, such as Fast R-CNN and Mask R-CNN.