{"work":{"id":"0d7ba565-3e2a-4dff-aa24-13faf1f6e69e","openalex_id":null,"doi":null,"arxiv_id":"1506.01497","raw_key":null,"title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","authors":null,"authors_text":"Shaoqing Ren, Kaiming He, Ross B","year":2015,"venue":"cs.CV","abstract":"State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN 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, our detection system has a frame rate of 5fps (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.","external_url":"https://arxiv.org/abs/1506.01497","cited_by_count":null,"metadata_source":"pith","metadata_fetched_at":"2026-05-25T18:51:08.765062+00:00","pith_arxiv_id":"1506.01497","created_at":"2026-05-10T08:48:01.427155+00:00","updated_at":"2026-05-25T18:51:08.765062+00:00","title_quality_ok":true,"display_title":"Girshick, and Jian Sun","render_title":"Girshick, and Jian Sun"},"hub":{"state":{"work_id":"0d7ba565-3e2a-4dff-aa24-13faf1f6e69e","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":25,"external_cited_by_count":null,"distinct_field_count":5,"first_pith_cited_at":"2018-04-08T22:27:57+00:00","last_pith_cited_at":"2026-05-12T17:59:59+00:00","author_build_status":"not_needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"not_needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-05-29T20:00:19.288026+00:00","tier_text":"hub"},"tier":"hub","role_counts":[{"context_role":"background","n":2},{"context_role":"dataset","n":1},{"context_role":"method","n":1}],"polarity_counts":[{"context_polarity":"background","n":2},{"context_polarity":"use_dataset","n":1},{"context_polarity":"use_method","n":1}],"runs":{},"summary":{},"graph":{},"authors":[]}}