Region Based Convolutional Neural Networks: Difference between revisions

Revision as of 06:15, 7 August 2025

Latest revision as of 07:39, 27 August 2025

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* November 2013: '''R-CNN'''.<ref name=":2" />

* April 2015: '''Fast R-CNN'''.<ref name=":3">{{Cite book |last=Girshick |first=Ross |chapter=Fast R-CNN |date=7–13 December 2015 |title=2015 IEEE International Conference on Computer Vision (ICCV) |chapter-url=https://ieeexplore.ieee.org/document/7410526 |publisher=IEEE |pages=1440–1448 |doi=10.1109/ICCV.2015.169 |isbn=978-1-4673-8391-2}}</ref>

* June 2015: '''Faster R-CNN'''.<ref name=":4">{{Cite journal |last1=Ren |first1=Shaoqing |last2=He |first2=Kaiming |last3=Girshick |first3=Ross |last4=Sun |first4=Jian |date=2017-06-01 |title=Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks |url=https://ieeexplore.ieee.org/document/7485869 |journal=IEEE Transactions on Pattern Analysis and Machine Intelligence |volume=39 |issue=6 |pages=1137–1149 |doi=10.1109/TPAMI.2016.2577031 |pmid=27295650 |issn=0162-8828|arxiv=1506.01497 |bibcode=2017ITPAM..39.1137R }}</ref>

* March 2017: '''Mask R-CNN'''.<ref name=":5">{{Cite book |last1=He |first1=Kaiming |last2=Gkioxari |first2=Georgia |last3=Dollar |first3=Piotr |last4=Girshick |first4=Ross |chapter=Mask R-CNN |date=October 2017 |title=2017 IEEE International Conference on Computer Vision (ICCV) |chapter-url=https://ieeexplore.ieee.org/document/8237584 |publisher=IEEE |pages=2980–2988 |doi=10.1109/ICCV.2017.322 |isbn=978-1-5386-1032-9}}</ref>

* December 2017: '''Cascade R-CNN''' is trained with increasing Intersection over Union (IoU, also known as the [[Jaccard index]]) thresholds, making each stage more selective against nearby false positives.<ref>{{Cite journal |last1=Cai |first1=Zhaowei |last2=Vasconcelos |first2=Nuno |date=2017 |title=Cascade R-CNN: Delving into High Quality Object Detection |url=https://arxiv.org/abs/1712.00726 |arxiv=1712.00726 }}</ref>

* June 2019: '''Mesh R-CNN''' adds the ability to generate a 3D mesh from a 2D image.<ref>{{Cite journal |last1=Gkioxari |first1=Georgia |last2=Malik |first2=Jitendra |last3=Johnson |first3=Justin |date=2019 |title=Mesh R-CNN |url=https://openaccess.thecvf.com/content_ICCV_2019/html/Gkioxari_Mesh_R-CNN_ICCV_2019_paper.html |pages=9785–9795|arxiv=1906.02739 }}</ref>

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=== R-CNN ===

[[File:R-cnn.svg|thumb|272x272px|R-CNN architecture]]

Given an input image, R-CNN begins by applying 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 {{nobr|two thousand}} ROIs. After that, each ROI is fed through a neural network to produce output features. For each ROI's output features, an ensemble of [[support-vector machine]] classifiers is used to determine what type of object (if any) is contained within the ROI.<ref name=":2">{{Cite journal |last1=Girshick |first1=Ross |last2=Donahue |first2=Jeff |last3=Darrell |first3=Trevor |last4=Malik |first4=Jitendra |date=2016-01-01 |title=Region-Based Convolutional Networks for Accurate Object Detection and Segmentation |url=https://ieeexplore.ieee.org/document/7112511 |journal=IEEE Transactions on Pattern Analysis and Machine Intelligence |volume=38 |issue=1 |pages=142–158 |doi=10.1109/TPAMI.2015.2437384 |pmid=26656583 |bibcode=2016ITPAM..38..142G |issn=0162-8828|url-access=subscription }}</ref>

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