Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Paper summary _Objective:_ Improve on Fast R-CNN and [SPPnet](https://arxiv.org/abs/1406.4729) by incorporating the region proposal network directly. _Dataset:_ [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/) and [COCO](http://mscoco.org/). Both Fast R-CNN and SPPnet takes as input an image and several possibles objects (corresponding to regions of interest) and score each of them. They are thus two different entities: 1. A region proposal network. 2. A classification/detection network (Fast R-CNN/SSPnet). ## Architecture: First image features are extracted using a state of the art ConvNet, then they are used for both Region proposal and actual detection/classification on those regions. [![screen shot 2017-04-14 at 2 59 28 pm](https://cloud.githubusercontent.com/assets/17261080/25043807/01a287b6-2123-11e7-944c-01493371df29.png)](https://cloud.githubusercontent.com/assets/17261080/25043807/01a287b6-2123-11e7-944c-01493371df29.png) ## Results: By incorporating the region proposal network right after the feature ConvNet its computation cost becomes basically free which leads to an elegant solution (only one network) but more importantly greatly improve speed at test time.
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scholar.google.com
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, Shaoqing and He, Kaiming and Girshick, Ross B. and Sun, Jian
Neural Information Processing Systems Conference - 2015 via Local Bibsonomy
Keywords: dblp


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