{"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/crack-segmentation-for-low-resolution-images","title":"Crack Segmentation for Low-Resolution Images using Joint Learning with Super-Resolution","arxiv_id":null,"date":"2021-07-25","proceeding":"International Conference on Machine Vision and Applications (MVA) 2021","authors":["Yuki Kondo","Norimichi Ukita"],"abstract":"This paper proposes a method for crack segmentation on low-resolution images. Detailed cracks on their high-resolution images are estimated by super resolution from the low-resolution images. Our proposed method optimizes super-resolution images for the crack segmentation. For this method, we propose the Boundary Combo loss to express the local details of the crack. Experimental results demonstrate that our method outperforms the combinations of other previous approaches.","url_abs":"https://ieeexplore.ieee.org/abstract/document/9511400","url_pdf":"http://www.mva-org.jp/Proceedings/2021/papers/O1-1-2.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":"crack-segmentation-for-low-resolution-images","repo_url":"https://github.com/Yuki-11/CSSR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"crack-segmentation","task_name":"Crack Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/crack-segmentation-on-khanhha-s-dataset-4x","task":"Crack Segmentation","dataset":"khanhha's dataset - 4x upscaling","model":"CSSR (SS→SR)","rank_in_archive_order":1,"of":2,"metrics":{"Average IOU":"0.558","IoU_max":"0.558"},"uses_additional_data":true},{"leaderboard":"/sota/crack-segmentation-on-khanhha-s-dataset-4x","task":"Crack Segmentation","dataset":"khanhha's dataset - 4x upscaling","model":"CSSR (SR→SS)","rank_in_archive_order":2,"of":2,"metrics":{"Average IOU":"0.518","IoU_max":"0.587"},"uses_additional_data":true},{"leaderboard":"/sota/crack-segmentation-on-khanhha-s-dataset-4x-1","task":"Crack Segmentation","dataset":"khanhha's dataset - 4x upscaling (blind)","model":"CSSR (w/ PSPNet)","rank_in_archive_order":3,"of":7,"metrics":{"AHD95":"24.74","Average IOU":"0.539","HD95_min":"21.20","IoU_max":"0.557"},"uses_additional_data":true}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}