In the second step of the Region-based Convolutional Neural Network (R-CNN) model, a pretrained Convolutional Neural Network (CNN) is chosen and truncated prior to its output layer. Each region proposal extracted from the image is then resized to match the required input dimensions of this network. Finally, the CNN performs forward propagation on these resized regions to extract and output their features.

R-CNN Feature Extraction

The first step in the Region-based Convolutional Neural Network (R-CNN) pipeline is to perform selective search to extract multiple high-quality region proposals from the input image. These regions are typically selected at various scales, shapes, and sizes. Each of these region proposals is then labeled with a ground-truth class and a bounding box to be used in subsequent stages.

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The Region-based Convolutional Neural Network (R-CNN) is an object detection model that operates by first extracting a large number of region proposals (e.g., $$2000$$) from an input image. Anchor boxes can also be considered as such proposals. A Convolutional Neural Network (CNN) is then used to perform forward propagation on each individual region proposal to extract its features. Finally, these features are utilized to predict the class and bounding box for each proposed region.

Region-based Convolutional Neural Network

Selective search is a computer vision algorithm utilized to extract multiple high-quality region proposals from an input image. By selecting proposed regions at multiple scales and with varying shapes and sizes, the algorithm provides a set of candidate bounding boxes that can subsequently be evaluated by an object detection model.

Selective Search

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Selective Search in R-CNN

Although the Region-based Convolutional Neural Network (R-CNN) model effectively utilizes pretrained Convolutional Neural Networks (CNNs) to extract features, it suffers from significant computational inefficiency. Because the model relies on extracting thousands of region proposals from a single input image, it must execute thousands of separate CNN forward propagations to perform object detection. This massive computational burden makes the standard R-CNN architecture too slow and infeasible for widespread use in real-world applications.

Computational Bottleneck of R-CNN

In object detection, a region proposal is a candidate bounding box extracted from an input image that potentially contains an object of interest. Models typically extract a large number of these proposed regions (e.g., $$2000$$ per image) to process further. Additionally, predefined anchor boxes can also serve as region proposals.

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