{"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/frnet-factorized-and-regular-blocks-network","title":"FRNet: Factorized and Regular Blocks Network for Semantic Segmentation in Road Scene","arxiv_id":null,"date":"2022-04-01","proceeding":"IEEE Transactions on Intelligent Transportation Systems 2022 4","authors":["Mengxu Lu; Zhenxue Chen; Q. M. Jonathan Wu; Nannan Wang; Xuewen Rong; Xinghe Yan"],"abstract":"Nowadays, semantic segmentation methods for systems in road scene have a great demand. Most existing methods focus on high accuracy with low inference speed. And some approaches emphasize on speed, significantly sacrificing model accuracy. To make a trade-off between accuracy and inference speed, we propose a real-time network for semantic segmentation titled Factorized and Regular Network (FRNet), which employs an asymmetric encoder-decoder architecture with Factorized and Regular (FR) blocks. Our method achieves 70.4% mIoU on the Cityscapes test set with 1 million parameters at a speed of 127 frames per second (FPS) on a single Titan Xp at a resolution of 512×1024 . We evaluate FRNet on Cityscapes, Camvid, Kitti, and Gatech datasets to identify that our network stands out from other state-of-the-art networks.","url_abs":"https://ieeexplore.ieee.org/document/9275365","url_pdf":"https://ieeexplore.ieee.org/document/9275365","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":"frnet-factorized-and-regular-blocks-network","repo_url":"https://github.com/lu123456789/FRNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}