{"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":1.6326634574855585,"base_score":10.884423049903722,"endowment":10.884423049903722,"self_citation_contribution":1.6326634574855585,"citation_network_contribution":0.0,"self_endowment_contribution":1.6326634574855585,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":53338,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.052,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2017-06-01","fair_score":61.25,"fair_percentile":90.9,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","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,"fair_f":100.0,"fair_a":70.0,"fair_i":50.0,"fair_r":25.0,"fair_zscore":null,"fair_rationale":{"fair_score":61.25,"has_llm":false,"dimensions":{"F":{"name":"Findable","score":100.0,"criteria":[{"key":"f_has_doi","label":"Has a persistent DOI","kind":"deterministic","weight":1.0,"fraction":1.0,"signal":"DOI present","rationale":null},{"key":"f_repository_presence","label":"Indexed in repositories / literature DBs","kind":"deterministic","weight":1.0,"fraction":1.0,"signal":"datacite=25, pmcid=False, pmid=True","rationale":null},{"key":"f_persistent_ids","label":"Resolvable scholarly identifiers (OpenAlex)","kind":"deterministic","weight":0.5,"fraction":1.0,"signal":"OpenAlex id present","rationale":null}]},"A":{"name":"Accessible","score":70.0,"criteria":[{"key":"a_open_access","label":"Open Access / files deposited","kind":"deterministic","weight":1.5,"fraction":0.5,"signal":"files/OA location present but not flagged OA","rationale":null},{"key":"a_retrievable","label":"Free full text retrievable","kind":"deterministic","weight":1.0,"fraction":1.0,"signal":"4 OA location(s)","rationale":null}]},"I":{"name":"Interoperable","score":50.0,"criteria":[{"key":"i_linked_data","label":"Linked datasets / DataCite relations","kind":"deterministic","weight":1.0,"fraction":1.0,"signal":"linked_datasets=25, datacite=25","rationale":null},{"key":"i_standard_ids","label":"References data via standard accessions","kind":"deterministic","weight":1.0,"fraction":0.0,"signal":"accessions=0, trials=0","rationale":null}]},"R":{"name":"Reusable","score":25.0,"criteria":[{"key":"r_license","label":"Clear, open reuse license","kind":"deterministic","weight":1.5,"fraction":0.5,"signal":"license present (https://doi.org/10.15223/policy-029)","rationale":null},{"key":"r_downloads","label":"Demonstrated reuse (downloads)","kind":"deterministic","weight":0.5,"fraction":0.0,"signal":"downloads=0","rationale":null},{"key":"r_version","label":"Versioned / maintained","kind":"deterministic","weight":0.5,"fraction":0.0,"signal":"no version chain","rationale":null},{"key":"r_dataset","label":"Classified as a data resource","kind":"deterministic","weight":0.5,"fraction":0.0,"signal":"not a dataset","rationale":null}]}},"suggestions":["Reference data using standard accessions (e.g. GEO, PDB, ClinicalTrials.gov).","Maintain explicit versioning for the dataset.","Make the paper/data Open Access or deposit the files in an open repository.","Attach a clear, open reuse license (e.g. CC-BY or CC0)."],"model":null,"agent_version":"fair_agent_v3","fulltext_source":"oa_pdf"},"fair_model":null,"fair_agent_version":"fair_agent_v3","fair_fulltext_source":"oa_pdf","fair_has_llm":false,"fair_computed_at":"2026-06-27T17:04:52.675476Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}