{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/fast-yolo-a-fast-you-only-look-once-system","title":"Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video","arxiv_id":"1709.05943","date":"2017-09-18","proceeding":null,"authors":["Mohammad Javad Shafiee","Brendan Chywl","Francis Li","Alexander Wong"],"abstract":"Object detection is considered one of the most challenging problems in this\nfield of computer vision, as it involves the combination of object\nclassification and object localization within a scene. Recently, deep neural\nnetworks (DNNs) have been demonstrated to achieve superior object detection\nperformance compared to other approaches, with YOLOv2 (an improved You Only\nLook Once model) being one of the state-of-the-art in DNN-based object\ndetection methods in terms of both speed and accuracy. Although YOLOv2 can\nachieve real-time performance on a powerful GPU, it still remains very\nchallenging for leveraging this approach for real-time object detection in\nvideo on embedded computing devices with limited computational power and\nlimited memory. In this paper, we propose a new framework called Fast YOLO, a\nfast You Only Look Once framework which accelerates YOLOv2 to be able to\nperform object detection in video on embedded devices in a real-time manner.\nFirst, we leverage the evolutionary deep intelligence framework to evolve the\nYOLOv2 network architecture and produce an optimized architecture (referred to\nas O-YOLOv2 here) that has 2.8X fewer parameters with just a ~2% IOU drop. To\nfurther reduce power consumption on embedded devices while maintaining\nperformance, a motion-adaptive inference method is introduced into the proposed\nFast YOLO framework to reduce the frequency of deep inference with O-YOLOv2\nbased on temporal motion characteristics. Experimental results show that the\nproposed Fast YOLO framework can reduce the number of deep inferences by an\naverage of 38.13%, and an average speedup of ~3.3X for objection detection in\nvideo compared to the original YOLOv2, leading Fast YOLO to run an average of\n~18FPS on a Nvidia Jetson TX1 embedded system.","url_abs":"http://arxiv.org/abs/1709.05943v1","url_pdf":"http://arxiv.org/pdf/1709.05943v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fast-yolo-a-fast-you-only-look-once-system","repo_url":"https://github.com/Vonski/wdi19","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":null,"task_name":"GPU"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-localization","task_name":"Object Localization"},{"task_slug":"real-time-object-detection","task_name":"Real-Time Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"darknet-19","method_name":"Darknet-19"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"speed","method_name":"SPEED"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"yolov2","method_name":"YOLOv2"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1709.05943","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}