{"doi":"10.1109/tpami.2016.2577031","title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","abstract":"State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features-using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model [3] , our detection system has a frame rate of 5 fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.","journal":"IEEE Transactions on Pattern Analysis and Machine Intelligence","year":2017,"id":5879,"datarank":19.336272714583693,"base_score":10.884423049903722,"endowment":10.884423049903722,"self_citation_contribution":1.6326634574855585,"citation_network_contribution":17.703609257098133,"self_endowment_contribution":1.6326634574855585,"citer_contribution":17.703609257098133,"corpus_percentile":99.0,"corpus_rank":115,"citation_count":53338,"citer_count":183,"citers_with_citation_signal":183,"citers_with_endowment":183,"datacite_reuse_total":0,"is_dataset":false,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2017-06-01","authors":[{"id":11162,"name":"Kaiming He","orcid":"0000-0001-7318-9658","position":1,"is_corresponding":false},{"id":2013,"name":"Ross Girshick","orcid":null,"position":2,"is_corresponding":false},{"id":18665,"name":"Jian Sun","orcid":"0000-0001-6270-2698","position":3,"is_corresponding":false},{"id":18664,"name":"Shaoqing Ren","orcid":null,"position":0,"is_corresponding":true}],"reference_count":40,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}