Fast R-CNN
Overview
Fast R-CNN is a convolutional neural network (CNN) based object detection algorithm. It was proposed by Ross Girshick in 2015 as an improvement over the previous R-CNN and SPP-net algorithms. Fast R-CNN addresses some of the key issues in its predecessors, such as training speed, detection speed, and the need for disk storage during training.
Architecture
The architecture of Fast R-CNN consists of three main components: a convolutional neural network, Region of Interest (RoI) pooling layer, and fully connected layers.
Convolutional Neural Network
The first component is a deep convolutional neural network. Fast R-CNN can use any pre-trained CNN as a feature extractor, such as VGG16 or ZF Net. This CNN takes an entire image as input and outputs a convolutional feature map.
Region of Interest (RoI) Pooling Layer
The second component is the Region of Interest (RoI) pooling layer. This layer takes the output feature map from the CNN and a set of RoIs as input. The RoIs are proposed by a region proposal algorithm such as Selective Search. The RoI pooling layer then performs max pooling on the input feature map for each RoI, producing a fixed-size feature map (e.g., 7x7). This allows the network to handle RoIs of different sizes.
Fully Connected Layers
The final component of the Fast R-CNN architecture is a sequence of fully connected layers. These layers take the fixed-size feature map output by the RoI pooling layer and produce two outputs: a class label and a bounding box for the object. The class label is produced by a softmax layer, and the bounding box is produced by a linear regression layer.
Training
Fast R-CNN is trained using a multi-task loss function. This loss function combines the classification loss (measuring the error in predicting the object class) and the regression loss (measuring the error in predicting the bounding box coordinates). The multi-task loss function allows Fast R-CNN to be trained end-to-end in a single stage, which is a key improvement over the previous R-CNN and SPP-net algorithms.
Performance
Fast R-CNN significantly improves the training and detection speed compared to its predecessors. It also eliminates the need for disk storage during training, as it can be trained directly on the GPU. In terms of detection accuracy, Fast R-CNN achieves comparable or better results than the previous state-of-the-art methods on several benchmark datasets, such as PASCAL VOC and MS COCO.
Limitations and Further Developments
Despite its improvements, Fast R-CNN still relies on a separate region proposal algorithm, which can be slow and is not learned end-to-end with the rest of the network. This limitation was addressed by the subsequent Faster R-CNN algorithm, which introduces a Region Proposal Network (RPN) that shares convolutional features with the detection network, allowing the entire system to be trained end-to-end.