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.

Selective Search in R-CNN

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.

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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.

Region Proposal

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Selective Search

The region proposal network (RPN) is an internal component of Faster R-CNN that generates region proposals by processing CNN-extracted feature maps in four steps. First, a $$3 	imes 3$$ convolutional layer with a padding of $$1$$ transforms the feature maps into a new output with $$c$$ channels, providing a length-$$c$$ feature vector for each spatial unit. Second, multiple anchor boxes of varying scales and aspect ratios are generated and centered on each pixel. Third, these length-$$c$$ feature vectors are used to predict a binary class (object or background) and a bounding box for each anchor box. Finally, non-maximum suppression is applied to the bounding boxes predicted as objects to remove overlapping results, yielding the final region proposals required by the region of interest pooling layer.

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