What is R-CNN?
R-CNN, which stands for Region-based Convolutional Neural Network, is a type of deep learning model used for object detection in computer vision tasks. The term “R-CNN” actually refers to a family of models that share a common approach to object detection. The key idea behind R-CNN is to divide the object detection task into two stages: region proposal and object classification.
How does R-CNN work?
- Region Proposal Network (RPN): In the first stage, the model generates a set of region proposals that are likely to contain objects. These proposals are potential bounding boxes around objects in the image. The region proposal network is responsible for suggesting these candidate regions.
- Region of Interest (RoI) Pooling: Once the region proposals are generated, each region is cropped from the image and resized to a fixed size. This process is known as RoI pooling, and it ensures that the region of interest is consistently represented in a fixed-size feature map, regardless of the size or aspect ratio of the original region proposal.
- Feature Extraction: The cropped and resized regions are then passed through a pre-trained convolutional neural network (CNN) to extract features from each region.
- Object Classification and Bounding Box Regression: The features extracted from each region are used for two tasks: object classification and bounding box regression. Object classification involves determining the class of the object within the region, and bounding box regression refines the coordinates of the bounding box around the object.
Later, Fast R-CNN was developed to enhance the speed and efficiency of the object detection process. The main issues with R-CNN are its slow training and inference times due to the need to independently process each region proposal using the CNN.
Mask R-CNN | ML
The article provides a comprehensive understanding of the evolution from basic Convolutional Neural Networks (CNN) to the sophisticated Mask R-CNN, exploring the iterative improvements in object detection, instance segmentation, and the challenges and advantages associated with each model.