Fast R-CNN is a very important algorithm in the field of object detection. It is an improved version of the technique called R-CNN (Regions with CNN features), designed to significantly enhance speed while maintaining accuracy. This course will cover the basic concepts of Fast R-CNN, key components, and practical examples using PyTorch.
1. Overview of Fast R-CNN
Fast R-CNN is a deep learning model that takes an image as input, detects each object, and outputs a bounding box for each object. The key idea of Fast R-CNN is to pass the entire image through the CNN (Convolutional Neural Network) only once. This solves the speed issues that R-CNN had.
1.1. Features of Fast R-CNN
- Global Feature Map: It processes the input image through the CNN to generate an overall feature map.
- RoI Pooling: It extracts fixed-size features from the object candidate regions.
- Fast Learning: Rapid progression is possible using SGD (Stochastic Gradient Descent) and an end-to-end learning approach.
- Softmax Classification: It provides two outputs: classifying the type of object and refining the bounding box.
2. Structure of Fast R-CNN
Fast R-CNN consists of four main stages. The first stage is the generation of feature maps through the CNN. The second stage is extracting candidate regions. The third stage performs RoI pooling on each candidate region to generate fixed-size features. Finally, the last stage generates the final output through softmax classification and bounding box regression.
2.1. Feature Map Generation through CNN
The input image is passed through the CNN to generate feature maps. Pre-trained models such as VGG16 or ResNet are generally used to maximize performance.
2.2. Extraction of Candidate Regions
Fast R-CNN uses methods like Selective Search (not Region Proposal Network) to extract candidate regions. These candidate regions are converted into fixed-size feature vectors through RoI pooling in the subsequent steps.
2.3. RoI Pooling
In the RoI pooling stage, the feature maps corresponding to the candidate regions are transformed into a fixed size. This allows regions of various sizes to be converted into tensors of the same size for processing by the network.
2.4. Final Classification and Bounding Box Regression
Finally, the features generated through RoI pooling are passed through two separate Fully Connected Layers. One is a Softmax Layer for class prediction, and the other is a regression layer for adjusting the bounding boxes.
3. Implementation of Fast R-CNN
Now that we understand the structure of Fast R-CNN, let’s implement a basic Fast R-CNN model using PyTorch. The code below focuses on constructing the basic structure of Fast R-CNN.