The Region-Based Convolutional Neural Network (RCNN) is a computer vision model used for object detection tasks. It was introduced by Ross Girshick, et al., in the paper titled "Rich feature hierarchies for accurate object detection and semantic segmentation" (2014).
RCNN is a two-stage object detection framework that combines the power of Convolutional Neural Networks (CNNs) and region proposal algorithms. The key idea behind RCNN is to leverage selective search or similar methods to generate region proposals in an image and then use a CNN to extract features from each proposed region. These region-specific features are then used for object classification and bounding box regression.
RCNN consists of the following key components:
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Region Proposal: A region proposal algorithm is used to generate potential object regions in an image. Selective search is often used for this purpose.
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Feature Extraction: Once the regions are proposed, a CNN is applied to each region to extract features. These features are then flattened and passed through fully connected layers.
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Object Classification: The extracted features are used to classify objects within each proposed region into predefined categories or classes.
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Bounding Box Regression: In addition to classification, RCNN performs bounding box regression to refine the object's location within each proposed region.
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RCNN demonstrates strong performance in object detection tasks and is one of the early models that paved the way for modern object detection architectures.
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It provides region-specific features, enabling accurate localization and classification of objects.