{"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/s2d2net-an-improved-approach-for-robust-steel","title":"S2D2Net: An Improved Approach For Robust Steel Surface Defects Diagnosis With Small Sample Learning","arxiv_id":null,"date":"2021-08-23","proceeding":"IEEE International Conference on Image Processing (ICIP) 2021 8","authors":["Vikanksh Nath","Chiranjoy Chattopadhyay"],"abstract":"Surface defect recognition of products is a necessary process to guarantee the quality of industrial production. This paper proposes a hybrid model, S2D2Net (Steel Surface Defect Diagnosis Network), for an efficient and robust inspection of the steel surface during the manufacturing process. The S2D2Net uses a pretrained ImageNet model as a feature extractor and learns a Capsule Network over the extracted features. The experimental results on a publicly available steel surface defect dataset (NEU) show that S2D2Net achieved 99.17% accuracy with minimal training data and improved by 9.59% over its closest competitor based on GAN. S2D2Net proved its robustness by achieving 94.7% accuracy on a diversity enhanced dataset, ENEU, and improved by 3.6% over its closest competitor. It has better, robust recognition performance compared to other state-of-the-art DNN-based detectors.","url_abs":"https://ieeexplore.ieee.org/document/9506405","url_pdf":"https://ieeexplore.ieee.org/document/9506405","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":"s2d2net-an-improved-approach-for-robust-steel","repo_url":"https://github.com/vikxoxo/S2D2Net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"defect-detection","task_name":"Defect Detection"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"robust-classification","task_name":"Robust classification"},{"task_slug":"small-data","task_name":"Small Data Image Classification"},{"task_slug":"weakly-supervised-defect-detection","task_name":"Weakly Supervised Defect Detection"}],"methods":[{"method_slug":"fixcaps","method_name":"Capsule Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}