{"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/cure-tsr-challenging-unreal-and-real","title":"CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition","arxiv_id":"1712.02463","date":"2017-12-07","proceeding":null,"authors":["Dogancan Temel","Gukyeong Kwon","Mohit Prabhushankar","Ghassan AlRegib"],"abstract":"In this paper, we investigate the robustness of traffic sign recognition\nalgorithms under challenging conditions. Existing datasets are limited in terms\nof their size and challenging condition coverage, which motivated us to\ngenerate the Challenging Unreal and Real Environments for Traffic Sign\nRecognition (CURE-TSR) dataset. It includes more than two million traffic sign\nimages that are based on real-world and simulator data. We benchmark the\nperformance of existing solutions in real-world scenarios and analyze the\nperformance variation with respect to challenging conditions. We show that\nchallenging conditions can decrease the performance of baseline methods\nsignificantly, especially if these challenging conditions result in loss or\nmisplacement of spatial information. We also investigate the effect of data\naugmentation and show that utilization of simulator data along with real-world\ndata enhance the average recognition performance in real-world scenarios. The\ndataset is publicly available at https://ghassanalregib.com/cure-tsr/.","url_abs":"http://arxiv.org/abs/1712.02463v2","url_pdf":"http://arxiv.org/pdf/1712.02463v2.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":"cure-tsr-challenging-unreal-and-real","repo_url":"https://github.com/olivesgatech/CURE-TSR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"traffic-sign-recognition","task_name":"Traffic Sign Recognition"}],"methods":[],"datasets_introduced":[{"slug":"cure-tsr","name":"CURE-TSR","full_name":"CURE Traffic Sign Recognition"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1712.02463","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}