{"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/cfenet-an-accurate-and-efficient-single-shot","title":"CFENet: An Accurate and Efficient Single-Shot Object Detector for Autonomous Driving","arxiv_id":"1806.09790","date":"2018-06-26","proceeding":null,"authors":["Qijie Zhao","Tao Sheng","Yongtao Wang","Feng Ni","Ling Cai"],"abstract":"The ability to detect small objects and the speed of the object detector are\nvery important for the application of autonomous driving, and in this paper, we\npropose an effective yet efficient one-stage detector, which gained the second\nplace in the Road Object Detection competition of CVPR2018 workshop - Workshop\nof Autonomous Driving(WAD). The proposed detector inherits the architecture of\nSSD and introduces a novel Comprehensive Feature Enhancement(CFE) module into\nit. Experimental results on this competition dataset as well as the MSCOCO\ndataset demonstrate that the proposed detector (named CFENet) performs much\nbetter than the original SSD and the state-of-the-art method RefineDet\nespecially for small objects, while keeping high efficiency close to the\noriginal SSD. Specifically, the single scale version of the proposed detector\ncan run at the speed of 21 fps, while the multi-scale version with larger input\nsize achieves the mAP 29.69, ranking second on the leaderboard","url_abs":"http://arxiv.org/abs/1806.09790v2","url_pdf":"http://arxiv.org/pdf/1806.09790v2.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":"cfenet-an-accurate-and-efficient-single-shot","repo_url":"https://github.com/siddhanthaldar/PyTorch_Object_Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"non-maximum-suppression","method_name":"Non Maximum Suppression"},{"method_slug":"speed","method_name":"SPEED"},{"method_slug":"ssd","method_name":"SSD"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}