{"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/inconseg-residual-guided-fusion-with","title":"InconSeg: Residual-Guided Fusion With Inconsistent Multi-Modal Data for Negative and Positive Road Obstacles Segmentation","arxiv_id":null,"date":"2023-05-02","proceeding":"journal 2023 5","authors":["Zhen Feng ID","Yanning Guo ID","David Navarro-Alarcon ID","Yueyong Lyu ID","and Yuxiang Sun"],"abstract":"Segmentation of road obstacles, including negative and positive obstacles, is critical to the safe navigation of autonomous vehicles. Recent methods have witnessed an increasing interest in using multi-modal data fusion (e.g., RGB and depth/disparity images). Although improved segmentation accuracy has been achieved by these methods, we still find that their performance could be easily degraded if the two modalities have inconsistent information, for example, distant obstacles that can be viewed in RGB images but cannot be viewed in depth/disparity images. To address this issue, we propose a novel two-encoder-two-decoder RGB-depth/disparity multi-modal network with Residual-Guided Fusion modules. Different from most existing networks that fuse feature maps in encoders, we fuse feature maps in decoder. We also release a large-scale RGB-depth/disparity dataset recorded in both urban and rural environments with manually-labeled ground truth for both negative- and positive-obstacles segmentation. Extensive experimental results demonstrate that\r\nour network achieves state-of-the-art performance compared with other networks.","url_abs":"https://ieeexplore.ieee.org/document/10114585","url_pdf":"https://ieeexplore.ieee.org/document/10114585","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":"inconseg-residual-guided-fusion-with","repo_url":"https://github.com/lab-sun/inconseg","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"autonomous-vehicles","task_name":"Autonomous Vehicles"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"road-damage-detection","task_name":"Road Damage Detection"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/road-damage-detection-on-npo","task":"Road Damage Detection","dataset":"NPO","model":"InconSeg","rank_in_archive_order":1,"of":1,"metrics":{"mIoU":"83.88"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}