{"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/total-recall-understanding-traffic-signs","title":"Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural Networks","arxiv_id":"1808.10524","date":"2018-08-30","proceeding":null,"authors":["Sourajit Saha","Sharif Amit Kamran","Ali Shihab Sabbir"],"abstract":"Recognizing Traffic Signs using intelligent systems can drastically reduce\nthe number of accidents happening world-wide. With the arrival of Self-driving\ncars it has become a staple challenge to solve the automatic recognition of\nTraffic and Hand-held signs in the major streets. Various machine learning\ntechniques like Random Forest, SVM as well as deep learning models has been\nproposed for classifying traffic signs. Though they reach state-of-the-art\nperformance on a particular data-set, but fall short of tackling multiple\nTraffic Sign Recognition benchmarks. In this paper, we propose a novel and\none-for-all architecture that aces multiple benchmarks with better overall\nscore than the state-of-the-art architectures. Our model is made of residual\nconvolutional blocks with hierarchical dilated skip connections joined in\nsteps. With this we score 99.33% Accuracy in German sign recognition benchmark\nand 99.17% Accuracy in Belgian traffic sign classification benchmark. Moreover,\nwe propose a newly devised dilated residual learning representation technique\nwhich is very low in both memory and computational complexity.","url_abs":"http://arxiv.org/abs/1808.10524v2","url_pdf":"http://arxiv.org/pdf/1808.10524v2.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":"total-recall-understanding-traffic-signs","repo_url":"https://github.com/Sourajit2110/DilatedSkipTotalRecall","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"self-driving-cars","task_name":"Self-Driving Cars"},{"task_slug":"traffic-sign-recognition","task_name":"Traffic Sign Recognition"}],"methods":[{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}