Region Based Convolutional Neural Networks: Difference between revisions

Revision as of 23:05, 29 March 2020

Revision as of 23:07, 29 March 2020

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'''Region Based Convolutional Neural Networks (R-CNN)''' are a varietyfamily of machine learning models for [[computer vision]] and specifically [[object detection]].

== History ==

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The original goal of R-CNN was to take an input image and produce a set of bounding boxes as output, where the each bounding box contains an object and also the category (e.g. car or pedestrian) of the object. More recently, R-CNN has been extended to perform other computer vision tasks. The following covers some of the versions of R-CNN that have been developed.

* November 2013: '''R-CNN'''. Given an input image, R-CNN begins by applying a mechanism called Selective Search to extract [[Region of interest|regions of interest]] (ROI), where each ROI is a rectangle that may represent the boundary of an object in image. Depending on the scenario, there may be as many as two thousand ROIs. After that, each ROI is fed through a neural network to produce output features. For each ROI's output features, a collection of [[support-vector machine]] classifiers is used to determine what type of object (if any) is incontained within the ROI.<ref>{{Cite news|last=Ghandi|first=Rohith|url=https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e|title=R-CNN, Fast R-CNN, Faster R-CNN, YOLO — Object Detection Algorithms|date=July 9, 2018|work=Towards Data Science|access-date=March 12, 2020|url-status=live}}</ref>

* April 2015: '''Fast R-CNN'''. While the original R-CNN ranindependently computed the neural network features on each of as many as 2000two thousand regions of interest (ROI), Fast R-CNN runs the neural network once on the whole image. At the end of the network is a novel method called ROIPooling, which slices out each ROI from the network's output tensor, reshapes it, and classifies it. As in the original R-CNN, the Fast R-CNN uses Selective Search to generate its region proposals.<ref name=":0">{{Cite news|last=Bhatia|first=Richa|url=https://analyticsindiamag.com/what-is-region-of-interest-pooling/|title=What is region of interest pooling?|date=September 10, 2018|work=Analytics India|access-date=March 12, 2020|url-status=live}}</ref>

* June 2015: '''Faster R-CNN'''. While Fast R-CNN used Selective Search to generate ROIs, Faster R-CNN integrates the ROI generation into the neural network itself.<ref name=":0" />

* March 2017: '''Mask R-CNN'''. While previous versions of R-CNN focused on object detection, Mask R-CNN adds instance segmentation. Mask R-CNN also replaced ROIPooling with a new method called ROIAlign, which can represent fractions of a pixel.<ref>{{Cite news|last=Farooq|first=Umer|url=https://medium.com/@umerfarooq_26378/from-r-cnn-to-mask-r-cnn-d6367b196cfd|title=From R-CNN to Mask R-CNN|date=February 15, 2018|work=Medium|access-date=March 12, 2020|url-status=live}}</ref><ref>{{Cite news|last=Weng|first=Lilian|url=https://lilianweng.github.io/lil-log/2017/12/31/object-recognition-for-dummies-part-3.html|title=Object Detection for Dummies Part 3: R-CNN Family|date=December 31, 2017|work=Lil'Log|access-date=March 12, 2020|url-status=live}}</ref>