Region-based CNN (R-CNN)

Region-based CNN (R-CNN) is an object detection method that identifies and locates multiple objects within an image. It works by first generating several candidate regions, or “proposals,” that might contain objects. Each proposal is then processed by a neural network to extract features and classify the object, if present. This approach combines the strengths of region proposals with deep learning to accurately detect different objects, such as people, cars, or animals, and determine their positions, enabling applications like image analysis, surveillance, and autonomous vehicles.