{"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/revisiting-shadow-detection-a-new-benchmark","title":"Revisiting Shadow Detection: A New Benchmark Dataset for Complex World","arxiv_id":"1911.06998","date":"2019-11-16","proceeding":null,"authors":["Xiaowei Hu","Tianyu Wang","Chi-Wing Fu","Yitong Jiang","Qiong Wang","Pheng-Ann Heng"],"abstract":"Shadow detection in general photos is a nontrivial problem, due to the complexity of the real world. Though recent shadow detectors have already achieved remarkable performance on various benchmark data, their performance is still limited for general real-world situations. In this work, we collected shadow images for multiple scenarios and compiled a new dataset of 10,500 shadow images, each with labeled ground-truth mask, for supporting shadow detection in the complex world. Our dataset covers a rich variety of scene categories, with diverse shadow sizes, locations, contrasts, and types. Further, we comprehensively analyze the complexity of the dataset, present a fast shadow detection network with a detail enhancement module to harvest shadow details, and demonstrate the effectiveness of our method to detect shadows in general situations.","url_abs":"https://arxiv.org/abs/1911.06998v3","url_pdf":"https://arxiv.org/pdf/1911.06998v3.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":"revisiting-shadow-detection-a-new-benchmark","repo_url":"https://github.com/xw-hu/FSDNet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"revisiting-shadow-detection-a-new-benchmark","repo_url":"https://github.com/xw-hu/CUHK-Shadow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"shadow-detection","task_name":"Shadow Detection"}],"methods":[],"datasets_introduced":[{"slug":"cuhk-shadow","name":"CUHK-Shadow","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/shadow-detection-on-cuhk-shadow","task":"Shadow Detection","dataset":"CUHK-Shadow","model":"FSDNet (TIP 2021) (512x512)","rank_in_archive_order":10,"of":16,"metrics":{"BER":"8.84"},"uses_additional_data":false},{"leaderboard":"/sota/shadow-detection-on-cuhk-shadow","task":"Shadow Detection","dataset":"CUHK-Shadow","model":"FSDNet (TIP 2021) (256x256)","rank_in_archive_order":13,"of":16,"metrics":{"BER":"9.93"},"uses_additional_data":false},{"leaderboard":"/sota/shadow-detection-on-sbu","task":"Shadow Detection","dataset":"SBU / SBU-Refine","model":"FSDNet (TIP 2021) (512x512)","rank_in_archive_order":13,"of":16,"metrics":{"BER":"6.8"},"uses_additional_data":false},{"leaderboard":"/sota/shadow-detection-on-sbu","task":"Shadow Detection","dataset":"SBU / SBU-Refine","model":"FSDNet (TIP 2021) (256x256)","rank_in_archive_order":15,"of":16,"metrics":{"BER":"7.16"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1911.06998","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}