R-CNN, Fast R-CNN, and Faster R-CNN for Object Detection

Get started with R-CNN, Fast R-CNN, and Faster R-CNN for object detection with this comprehensive guide. Learn about the key differences between these methods and how to choose the right one for your specific task.

Object detection is crucial in computer vision, as it involves locating and identifying objects within an image or video. There are various approaches to object detection, including traditional methods such as Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF), as well as more recent deep learning-based methods such as R-CNN, Fast R-CNN, and Faster R-CNN. 

In this blog post, we will explore these three methods in-depth and provide a guide on how to get started with them for object detection. Whether you are a beginner in computer vision or an experienced practitioner looking to improve your object detection skills, this blog post offers everyone something.

Table of contents

• What is R-CNN?
• What is Fast R-CNN?
• What is Faster R-CNN?
• Getting Started with R-CNN, Fast R-CNN, and Faster R-CNN
  • Implementation
• Conclusion

What is R-CNN?

R-CNN, or Region-based Convolutional Neural Network, was introduced by Ross Girshick in 2014. It is a method for object detection that involves using a convolutional neural network (CNN) to classify object proposals, or regions of interest (ROIs), within an image. R-CNN is a two-stage object detection pipeline that first generates a set of ROIs using a method such as selective search or edge boxes and then classifies the objects within these ROIs using a CNN.

The R-CNN pipeline can be divided into three main steps:

1. Region proposal: A method, such as selective search or edge boxes, generates a set of ROIs within the image. The bounding boxes around the objects of interest typically define these ROIs.
2. Feature extraction: A CNN is used to extract features from each ROI. These features are then used to represent the ROI in a compact and informative manner.
3. Classification: The extracted features are fed into a classifier, such as a support vector machine (SVM), to predict the object’s class within the ROI.

One of the main advantages of R-CNN is that it can handle many object classes, as the classifier is trained separately for each class. However, a significant drawback of R-CNN is that it is computationally expensive, requiring the CNN to be run on each ROI individually.

Also read: Neural Network Online Courses & Certifications

What is Fast R-CNN?

Fast R-CNN, introduced by Ross Girshick in 2015, is an improvement over R-CNN that addresses the computational inefficiency of the original method. It achieves this by using a single CNN to process the entire image and generate the ROIs rather than running the CNN on each ROI individually.

The Fast R-CNN pipeline can be divided into four main steps:

1. Region proposal: A set of ROIs is generated using a method such as selective search or edge boxes.
2. Feature extraction: A CNN extracts features from the entire image.
3. ROI pooling: The extracted features are then used to compute a fixed-length feature vector for each ROI. This is done by dividing the ROI into a grid of cells and max-pooling the features within each cell.
4. Classification and bounding box regression: The fixed-length feature vectors for each ROI are fed into two fully connected (FC) layers: one for classification and one for bounding box regression. The classification FC layer predicts the object’s class within the ROI, while the bounding box regression FC layer predicts the refined bounding box coordinates for the object.

Fast R-CNN significantly reduces the computational cost of object detection compared to R-CNN, as the CNN is only run once on the entire image rather than multiple times on each ROI. However, it still requires a separate classifier for each object class, which can be computationally expensive if the number of classes is large.

Fast R-CNN has been widely used in various applications, including object detection in natural, medical, and satellite images. It has also been extended to handle tasks such as instance segmentation, joint object detection, and scene classification. 

What is Faster R-CNN?

Faster R-CNN, introduced by Shaoqing Ren et al. in 2015, is an improvement over Fast R-CNN that further reduces the computational cost of object detection. It achieves this by using a single CNN to generate both the ROIs and the features for each ROI, rather than using a separate CNN for each task, as in Fast R-CNN.

The Faster R-CNN pipeline can be divided into four main steps:

1. Feature extraction: A CNN extracts features from the entire image.
2. Region proposal: A set of ROIs is generated using a fully convolutional network (FCN) that processes the extracted features.
3. ROI pooling: The extracted features are then used to compute a fixed-length feature vector for each ROI using the same ROI pooling process as in Fast R-CNN.
4. Classification and bounding box regression: The fixed-length feature vectors for each ROI are fed into two separate FC layers: one for classification and one for bounding box regression. The classification FC layer predicts the object’s class within the ROI, while the bounding box regression FC layer predicts the refined bounding box coordinates for the object.

Faster R-CNN combines the feature extraction and region proposal steps of R-CNN and Fast R-CNN into a single CNN, making it more computationally efficient than both methods. It also uses an anchor box mechanism to handle multiple scales and aspect ratios, which can improve the robustness of object detection.

Getting Started with R-CNN, Fast R-CNN, and Faster R-CNN

Now that we understand the basic principles behind R-CNN, Fast R-CNN, and Faster R-CNN, let’s look at how to get started with these methods for object detection.

Prerequisites

Before diving into the implementation of these methods, there are a few prerequisites that you should be familiar with:

• Basic knowledge of convolutional neural networks (CNNs) and how they work
• Familiarity with everyday computer vision tasks such as image classification and object detection
• Basic understanding of Python programming and deep learning frameworks such as TensorFlow or PyTorch

Data Preparation

The first step in getting started with any object detection method is to prepare your data. This typically involves:

• Collecting and labeling a dataset of images that contain the objects you want to detect
• Splitting the dataset into training, validation, and test sets
• Preprocessing the images to resize and normalize them
• Creating a dataset object and iterator to feed the data into the model

Many open-source datasets are available for object detection, such as the PASCAL VOC and COCO datasets. You can also create your dataset by collecting and labeling images of your desired objects.

Implementation

Once your data is prepared, you can start implementing one of the object detection methods discussed in this blog post. Here are some general steps to follow:

1. Choose a deep learning framework, such as TensorFlow or PyTorch, and install it with any necessary dependencies.
2. Download a pre-trained CNN model, such as VGG16 or ResNet, and use it as the base model for feature extraction.
3. Implement the region proposal method, such as selective search or edge boxes, to generate the ROIs for each image.
4. Implement the ROI pooling layer to convert the extracted features into fixed-length feature vectors for each ROI.
5. Implement the classification and bounding box regression FC layers to predict the class and refine bounding box coordinates for each ROI.
6. Train the model using the training data and evaluate its performance on the validation and test sets.
7. Fine-tune the model by adjusting the hyperparameters, adding regularisation, and using data augmentation techniques.
8. Use the trained model to detect objects in new images or videos.

It is worth noting that the specific implementation details of R-CNN, Fast R-CNN, and Faster R-CNN can vary depending on the deep learning framework and the specific details of the architecture. Therefore, it is recommended to refer to the original papers or online tutorials for more detailed guidance on implementing these methods.

Evaluation

Once you have trained and fine-tuned your object detection model, it is essential to evaluate its performance to ensure that it can accurately detect and classify the desired objects. There are several standard metrics used to evaluate object detection models, including:

• Mean Average Precision (mAP): This metric measures the average precision of the model across all classes and all IoU (intersection over union) thresholds. It is a standard metric used to evaluate the performance of object detection models on the PASCAL VOC and COCO datasets.
• Average Precision (AP): This metric measures the precision of the model at a specific IoU threshold. It is often used with the mAP metric to evaluate the model’s performance at different IoU thresholds.
• Recall This metric measures the fraction of positive instances that the model correctly detected.
• False Positive Rate (FPR): This metric measures the fraction of negative instances that were incorrectly classified as positive by the model.

It is crucial to consider the trade-off between precision and recall when evaluating the performance of an object detection model, as a model with high precision may have low recall and vice versa.

Conclusion

In this blog post, we have discussed the principles behind R-CNN, Fast R-CNN, and Faster R-CNN, three popular methods for object detection. The main difference between these three algorithms is the way they generate region proposals. R-CNN uses selective search, Fast R-CNN uses RoI pooling and Faster R-CNN which uses a Region Proposal Network (RPN) to generate the region proposals directly from the CNN feature maps. With the correct data and a bit of programming knowledge, you can use these methods to detect and classify objects in images and videos accurately