Generally, region selection was typically performed using a method called “Selective Search,” as was the case in Fast RCNN. However, this process can be time-consuming. Faster RCNN was developed to address this issue by reducing the time spent on region selection.
In this article, we will explore the key features and principles of Faster RCNN, including its practical applications, advantages, and limitations. We will also delve into the details of how it works and its significance in the field of object detection. So make sure to pay close attention and read through the entire article to gain a comprehensive understanding of Faster RCNN.
What is Faster RCNN?
R-CNN (Regional Convolutional Neural Network) is a type of object detection algorithm that utilizes a CNN to identify objects in an image by analyzing regions of the image. The R in R-CNN stands for “region-based,” as the algorithm selects a region from the full image and processes it as a smaller, cropped version of the original image.
Fast RCNN and Faster RCNN are variations on the basic R-CNN model, with Fast RCNN being the precursor to Faster RCNN. Faster RCNN is composed of two components: Fast RCNN and a new network called the Region Proposal Network (RPN). Essentially, Faster RCNN combines the capabilities of Fast RCNN with the additional object proposal capabilities provided by the RPN. This allows Faster RCNN to be more efficient and accurate in object detection tasks.
Region Proposal Network (RPN)
The Region Proposal Network (RPN) is designed to reduce the running time of region proposal processes. The RPN was first introduced in the paper on Faster RCNN, and it has since become a widely used and effective method for generating high-quality region proposals.
Technically, the RPN is a network that is overlaid on the final feature map of the convolution layers in the Faster RCNN model. It takes in images of any size as input and generates a number of rectangular object proposals as output. The RPN uses anchor boxes, a classification layer, and a regression layer to help generate these object proposals.
By understanding how these layers function, we can gain insight into the inner workings of the RPN and how it contributes to the overall efficiency and accuracy of the Faster RCNN model.
The Working of Faster RCNN
Let’s now talk about how Faster RCNN functions.
- When an input image is processed by a CNN, feature maps are generated by applying filters to the image. These feature maps can be visualized to understand the features that the CNN is recognizing in the image. The RPN receives these feature maps as input.
- The RPN generates anchor boxes to represent the regions of the image that may contain objects. An anchor box is a reference box with a specific scale and aspect ratio, and a group of anchor boxes with various scales and ratios make up one region. The anchor boxes are useful because they help transmit features from the RPN to the Fast RCNN model, even though both the RPN and Fast RCNN are trained independently.
- After being generated by the RPN, the anchor boxes are passed through two layers: a classification layer and a regression layer. The classification layer classifies whether or not an object is present in the region represented by the anchor box. The regression layer then outputs bounding boxes around the object, with coordinates indicating the location of the object in the image. These bounding boxes are used to identify and localize objects in the input image.
Sharing and Training
The Fast RCNN and RPN networks in the Faster RCNN model operate on the same convolutional layers, with the help of anchor boxes. There are three options for training these networks:
- Alternating Training: This is the preferred method of training, in which the networks are trained alternately, with the RPN being trained first and then the Fast RCNN, and so on.
- Approximate Joint Training: In this method, both networks are treated as a single network and trained together, with the RPN producing the region proposals.
- Non-Approximate Joint Training: In this method, the gradients of the weights with respect to the proposed bounding boxes are determined using an RoI Warping layer, while most other calculations ignore the gradients.
Advantages of Faster RCNN
Faster RCNN has the following significant advantages:
- Faster RCNN is a trained end-to-end model.
- Faster RCNN is a single-stage model.
- Faster RCNN has a unified network.
- Many recently developed models, such as 3D object detection, are built on faster RCNN.
- RCNN is slower than Fast RCNN, which is slower than Faster RCNN.
- The Faster RCNN’s RPN component instructs the Fast RCNN on where to look.
- Compared to RCNN and Fast RCNN, Faster RCNN has a greater mAP.
- The approximate test time per image with proposals is 50 seconds for the RCNN, 2 seconds for the Fast RCNN, and 0.2 seconds for the Faster RCNN.
Applications of Faster RCNN
Faster RCNN has a variety of applications in real-world scenarios. Some examples of these applications include:
- Autonomous driving
- Smart surveillance systems
- Facial recognition
- Medical image analysis
- Robotics
- Augmented reality
Issues of Faster RCNN
There are a few issues that may be of concern when using Faster RCNN:
- Progressive examination of image regions: Faster RCNN does not examine the entire image in one pass, but rather focuses on regions of the image progressively. This can result in some objects being missed or classified incorrectly if they are not located in the initially examined regions of the image.
- Multiple runs through a single image: In order to extract all of the objects in an image, the Faster RCNN system may require multiple runs through the same image, which is time-consuming.
- Long processing times: When processing samples from the same image, the network may take a while to complete. This can be an issue in applications where fast processing times are required.
Final Thoughts
Faster R-CNN is a powerful and widely used object detection model that is capable of solving complex computer vision tasks. It utilizes RPN for effective and precise region proposal generation, and the use of shared convolutional layers helps to make the region proposal step almost cost-free.
Faster R-CNN has also served as the foundation for the development of other object detection models, such as Mask RCNN, which is able to locate specific pixels of each object rather than simply bounding boxes.
Understanding the mechanics of Faster R-CNN is crucial for those working in the field of computer vision, and it is a valuable tool for accurately and efficiently detecting and classifying objects in a wide range of real-world applications.
I hope that this article has helped to provide a clear understanding of the workings of Faster R-CNN.
