{"url":"/method/faster-r-cnn","slug":"faster-r-cnn","name":"Faster R-CNN","full_name":"Faster R-CNN","full_name_withheld":false,"description_markdown":"**Faster R-CNN** is an object detection model that improves on [Fast R-CNN](https://paperswithcode.com/method/fast-r-cnn) by utilising a region proposal network ([RPN](https://paperswithcode.com/method/rpn)) with the CNN model. The RPN shares full-image convolutional features with the detection network, enabling nearly cost-free region proposals. It 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](https://paperswithcode.com/method/fast-r-cnn) for detection. RPN and Fast [R-CNN](https://paperswithcode.com/method/r-cnn) are merged into a single network by sharing their convolutional features: the RPN component tells the unified network where to look.\r\n\r\nAs a whole, Faster R-CNN consists of two modules. The first module is a deep fully convolutional network that proposes regions, and the second module is the Fast R-CNN detector that uses the proposed regions.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","paper":"/paper/faster-r-cnn-towards-real-time-object","first_author":"Shaoqing Ren","n_authors":4,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/faster-r-cnn-towards-real-time-object"},"source":{"url":"http://arxiv.org/abs/1506.01497v3","title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/chenyuntc/simple-faster-rcnn-pytorch/blob/367db367834efd8a2bc58ee0023b2b628a0e474d/model/faster_rcnn.py#L22","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Object Detection Models","url":"/methods/category/object-detection-models","pwc_aliases":[]}],"n_papers_tagged":499,"archive_num_papers":499,"papers_newest_first":[{"paper":null,"title":"Pattern-Based Phase-Separation of Tracer and Dispersed Phase Particles in Two-Phase Defocusing Particle Tracking Velocimetry","date":"2025-06-22","arxiv_id":"2506.18157","n_code_links":0,"syntology":null},{"paper":null,"title":"Prmpt2Adpt: Prompt-Based Zero-Shot Domain Adaptation for Resource-Constrained Environments","date":"2025-06-20","arxiv_id":"2506.16994","n_code_links":0,"syntology":null},{"paper":"/paper/3d-gaussian-splat-vulnerabilities","title":"3D Gaussian Splat Vulnerabilities","date":"2025-05-30","arxiv_id":"2506.00280","n_code_links":1,"syntology":null},{"paper":null,"title":"AppleGrowthVision: A large-scale stereo dataset for phenological analysis, fruit detection, and 3D reconstruction in apple orchards","date":"2025-05-20","arxiv_id":"2505.14029","n_code_links":0,"syntology":null},{"paper":null,"title":"Object detection in adverse weather conditions for autonomous vehicles using Instruct Pix2Pix","date":"2025-05-13","arxiv_id":"2505.08228","n_code_links":0,"syntology":null},{"paper":null,"title":"FMNV: A Dataset of Media-Published News Videos for Fake News Detection","date":"2025-04-10","arxiv_id":"2504.07687","n_code_links":0,"syntology":null},{"paper":null,"title":"BBoxCut: A Targeted Data Augmentation Technique for Enhancing Wheat Head Detection Under Occlusions","date":"2025-03-31","arxiv_id":"2503.24032","n_code_links":0,"syntology":null},{"paper":"/paper/a-gan-enhanced-deep-learning-framework-for","title":"A GAN-Enhanced Deep Learning Framework for Rooftop Detection from Historical Aerial Imagery","date":"2025-03-29","arxiv_id":"2503.23200","n_code_links":1,"syntology":null},{"paper":null,"title":"Autonomous AI for Multi-Pathology Detection in Chest X-Rays: A Multi-Site Study in the Indian Healthcare System","date":"2025-03-28","arxiv_id":"2504.00022","n_code_links":0,"syntology":null},{"paper":null,"title":"Exploring Few-Shot Object Detection on Blood Smear Images: A Case Study of Leukocytes and Schistocytes","date":"2025-03-21","arxiv_id":"2503.17107","n_code_links":0,"syntology":null},{"paper":"/paper/walnutdata-a-uav-remote-sensing-dataset-of","title":"WalnutData: A UAV Remote Sensing Dataset of Green Walnuts and Model Evaluation","date":"2025-02-27","arxiv_id":"2502.20092","n_code_links":1,"syntology":null},{"paper":null,"title":"Automatic Vehicle Detection using DETR: A Transformer-Based Approach for Navigating Treacherous Roads","date":"2025-02-25","arxiv_id":"2502.17843","n_code_links":0,"syntology":null},{"paper":null,"title":"Autonomous Vision-Guided Resection of Central Airway Obstruction","date":"2025-02-25","arxiv_id":"2502.18586","n_code_links":0,"syntology":null},{"paper":null,"title":"Demystifying Catastrophic Forgetting in Two-Stage Incremental Object Detector","date":"2025-02-08","arxiv_id":"2502.05540","n_code_links":0,"syntology":null},{"paper":null,"title":"Adaptive Object Detection for Indoor Navigation Assistance: A Performance Evaluation of Real-Time Algorithms","date":"2025-01-30","arxiv_id":"2501.18444","n_code_links":0,"syntology":null},{"paper":null,"title":"Advanced technology in railway track monitoring using the GPR Technique: A Review","date":"2025-01-19","arxiv_id":"2501.11132","n_code_links":0,"syntology":null},{"paper":null,"title":"A method for estimating roadway billboard salience","date":"2025-01-13","arxiv_id":"2501.07342","n_code_links":0,"syntology":null},{"paper":"/paper/crrg-clip-automatic-generation-of-chest","title":"CRRG-CLIP: Automatic Generation of Chest Radiology Reports and Classification of Chest Radiographs","date":"2024-12-31","arxiv_id":"2501.01989","n_code_links":1,"syntology":null},{"paper":"/paper/simltd-simple-supervised-and-semi-supervised","title":"SimLTD: Simple Supervised and Semi-Supervised Long-Tailed Object Detection","date":"2024-12-28","arxiv_id":"2412.20047","n_code_links":1,"syntology":null},{"paper":"/paper/distortion-aware-adversarial-attacks-on","title":"Distortion-Aware Adversarial Attacks on Bounding Boxes of Object Detectors","date":"2024-12-25","arxiv_id":"2412.18815","n_code_links":1,"syntology":null},{"paper":null,"title":"Sampling Bag of Views for Open-Vocabulary Object Detection","date":"2024-12-24","arxiv_id":"2412.18273","n_code_links":0,"syntology":null},{"paper":"/paper/comprehensive-multi-modal-prototypes-are","title":"Comprehensive Multi-Modal Prototypes are Simple and Effective Classifiers for Vast-Vocabulary Object Detection","date":"2024-12-23","arxiv_id":"2412.17800","n_code_links":1,"syntology":null},{"paper":"/paper/real-time-tea-leaf-disease-detection-using","title":"Real-Time Tea Leaf Disease Detection Using Deep Learning-Based Models","date":"2024-12-20","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"title":"License Plate Detection and Character Recognition Using Deep Learning and Font Evaluation","date":"2024-12-17","arxiv_id":"2412.12572","n_code_links":0,"syntology":null},{"paper":null,"title":"Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking","date":"2024-12-05","arxiv_id":"2412.05331","n_code_links":0,"syntology":null},{"paper":null,"title":"Excretion Detection in Pigsties Using Convolutional and Transformerbased Deep Neural Networks","date":"2024-11-29","arxiv_id":"2412.00256","n_code_links":0,"syntology":null},{"paper":null,"title":"Multimodal Object Detection using Depth and Image Data for Manufacturing Parts","date":"2024-11-13","arxiv_id":"2411.09062","n_code_links":0,"syntology":null},{"paper":null,"title":"LAM-YOLO: Drones-based Small Object Detection on Lighting-Occlusion Attention Mechanism YOLO","date":"2024-11-01","arxiv_id":"2411.00485","n_code_links":0,"syntology":null},{"paper":null,"title":"TACO: Adversarial Camouflage Optimization on Trucks to Fool Object Detectors","date":"2024-10-28","arxiv_id":"2410.21443","n_code_links":0,"syntology":null},{"paper":null,"title":"How Important are Data Augmentations to Close the Domain Gap for Object Detection in Orbit?","date":"2024-10-21","arxiv_id":"2410.15766","n_code_links":0,"syntology":null}],"papers_shown":30,"tasks":[{"task":"/task/object-detection","name":"Object Detection","papers":326},{"task":"/task/object-detection-1","name":"object-detection","papers":303},{"task":"/task/object","name":"Object","papers":179},{"task":"/task/region-proposal","name":"Region Proposal","papers":38},{"task":"/task/image-classification","name":"Image Classification","papers":35},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":32},{"task":"/task/instance-segmentation","name":"Instance Segmentation","papers":24},{"task":"/task/image-classification","name":"image-classification","papers":24},{"task":"/task/classification","name":"General Classification","papers":23},{"task":"/task/deep-learning","name":"Deep Learning","papers":20},{"task":"/task/transfer-learning","name":"Transfer Learning","papers":19},{"task":"/task/data-augmentation","name":"Data Augmentation","papers":18},{"task":"/task/pedestrian-detection","name":"Pedestrian Detection","papers":17},{"task":"/task/autonomous-driving","name":"Autonomous Driving","papers":15},{"task":"/task/classification-1","name":"Classification","papers":14},{"task":"/task/domain-adaptation","name":"Domain Adaptation","papers":14},{"task":null,"name":"GPU","papers":13},{"task":"/task/object-recognition","name":"Object Recognition","papers":12},{"task":"/task/segmentation","name":"Segmentation","papers":12},{"task":"/task/2d-object-detection","name":"2D Object Detection","papers":11}],"tasks_shown":20,"n_tasks":302,"usage_by_year":[{"year":"2015","papers":3},{"year":"2016","papers":13},{"year":"2017","papers":32},{"year":"2018","papers":64},{"year":"2019","papers":70},{"year":"2020","papers":74},{"year":"2021","papers":78},{"year":"2022","papers":61},{"year":"2023","papers":42},{"year":"2024","papers":45},{"year":"2025","papers":17}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/faster-r-cnn"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}