{"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/ldc-lightweight-dense-cnn-for-edge-detection","title":"LDC: Lightweight Dense CNN for Edge Detection","arxiv_id":null,"date":"2022-06-27","proceeding":"IEEE Access 2022 6","authors":["Xavier Soria Poma","Gonzalo Pomboza-Junez","Angel Domingo Sappa"],"abstract":"This paper presents a Lightweight Dense Convolutional (LDC) neural network for edge detection. The proposed model is an adaptation of two state-of-the-art approaches, but it requires less than 4% of parameters in comparison with these approaches. The proposed architecture generates thin edge maps and reaches the highest score (i.e., ODS) when compared with lightweight models (models with less than 1 million parameters), and reaches a similar performance when compare with heavy architectures (models with about 35 million parameters). Both quantitative and qualitative results and comparisons with state-of-the-art models, using different edge detection datasets, are provided. The proposed LDC does not use pre-trained weights and requires straightforward hyper-parameter settings. The source code is released at https://github.com/xavysp/LDC.","url_abs":"https://ieeexplore.ieee.org/document/9807316","url_pdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9807316","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":"ldc-lightweight-dense-cnn-for-edge-detection","repo_url":"https://github.com/xavysp/LDC","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"edge-detection","task_name":"Edge Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/edge-detection-on-biped-1","task":"Edge Detection","dataset":"BIPED","model":"LDC","rank_in_archive_order":4,"of":6,"metrics":{"Number of parameters (M)":"674K","ODS":"0.889"},"uses_additional_data":false},{"leaderboard":"/sota/edge-detection-on-brind","task":"Edge Detection","dataset":"BRIND","model":"LDC","rank_in_archive_order":1,"of":3,"metrics":{"Number of parameters (M)":"674K","ODS":"0.790"},"uses_additional_data":false},{"leaderboard":"/sota/edge-detection-on-mdbd","task":"Edge Detection","dataset":"MDBD","model":"LDC","rank_in_archive_order":5,"of":6,"metrics":{"Number of parameters (M)":"674K","ODS":"0.880"},"uses_additional_data":false},{"leaderboard":"/sota/edge-detection-on-uded","task":"Edge Detection","dataset":"UDED","model":"LDC","rank_in_archive_order":3,"of":5,"metrics":{"ODS":"0.817"},"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}