Region-Based CNN (R-CNN) is a deep learning model designed for object detection in images. It combines the strengths of Convolutional Neural Networks (CNNs) with region proposal methods to identify and classify objects. R-CNN first generates potential bounding boxes, or regions of interest, in an image and then uses a CNN to extract features from these regions for classification. The R-CNN framework significantly improves detection accuracy by focusing on specific areas of an image rather than processing the entire image at once. This approach has inspired further advancements in object detection, leading to variations like Fast R-CNN and Faster R-CNN, which enhance speed and efficiency.