{"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/mfnet-multi-feature-fusion-network-for-real","title":"MFNet: Multi-Feature Fusion Network for Real-Time Semantic Segmentation in Road Scenes","arxiv_id":null,"date":"2022-11-01","proceeding":"IEEE Transactions on Intelligent Transportation Systems 2022 11","authors":["Mengxu Lu; Zhenxue Chen; Chengyun Liu; Sile Ma; Lei Cai; Hao Qin"],"abstract":"Although high-accuracy networks have been applied to semantic segmentation at present, their inference speeds remain slow. A trade-off between accuracy and speed is demanded for real-time applications. To approach this problem, we propose Multi-Feature Fusion Network (MFNet) with real-time efficient prediction capacity. MFNet adopts three branches (attention, semantic and spatial information) to capture low-level and high-level features. Additionally, MFNet exerts asymmetric factorized (AF) blocks to extract local and long-range features. As a result, without any pre-training or post-processing, MFNet using only 1.34 M parameters, achieves 72.1% mean intersection over union (mIoU) on the Cityscapes test set at a speed of 116 frames per second (FPS), with 512×1024 high resolution on a single Titan Xp graphics card. Our network’s performance stands out from other state-of-the-art networks on four datasets (Cityscapes, CamVid, KITTI, and Gatech).","url_abs":"https://ieeexplore.ieee.org/document/9839297","url_pdf":"https://ieeexplore.ieee.org/document/9839297","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":"mfnet-multi-feature-fusion-network-for-real","repo_url":"https://github.com/lu123456789/MFNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"real-time-semantic-segmentation","task_name":"Real-Time Semantic Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"speed","method_name":"SPEED"},{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}