{"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/novel-deep-learning-model-for-traffic-sign","title":"Novel Deep Learning Model for Traffic Sign Detection Using Capsule Networks","arxiv_id":"1805.04424","date":"2018-05-11","proceeding":null,"authors":["Amara Dinesh Kumar"],"abstract":"Convolutional neural networks are the most widely used deep learning\nalgorithms for traffic signal classification till date but they fail to capture\npose, view, orientation of the images because of the intrinsic inability of max\npooling layer.This paper proposes a novel method for Traffic sign detection\nusing deep learning architecture called capsule networks that achieves\noutstanding performance on the German traffic sign dataset.Capsule network\nconsists of capsules which are a group of neurons representing the\ninstantiating parameters of an object like the pose and orientation by using\nthe dynamic routing and route by agreement algorithms.unlike the previous\napproaches of manual feature extraction,multiple deep neural networks with many\nparameters,our method eliminates the manual effort and provides resistance to\nthe spatial variances.CNNs can be fooled easily using various adversary attacks\nand capsule networks can overcome such attacks from the intruders and can offer\nmore reliability in traffic sign detection for autonomous vehicles.Capsule\nnetwork have achieved the state-of-the-art accuracy of 97.6% on German Traffic\nSign Recognition Benchmark dataset (GTSRB).","url_abs":"http://arxiv.org/abs/1805.04424v1","url_pdf":"http://arxiv.org/pdf/1805.04424v1.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":"novel-deep-learning-model-for-traffic-sign","repo_url":"https://github.com/2K2A/2K2A.github.io","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"novel-deep-learning-model-for-traffic-sign","repo_url":"https://github.com/dineshresearch/Novel-Deep-Learning-Model-for-Traffic-Sign-Detection-Using-Capsule-Networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"novel-deep-learning-model-for-traffic-sign","repo_url":"https://github.com/wlawt/capsnet-trafficsigns","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"traffic-sign-detection","task_name":"Traffic Sign Detection"},{"task_slug":"traffic-sign-recognition","task_name":"Traffic Sign Recognition"}],"methods":[],"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}