Comparative Analysis of Different Machine Learning Techniques on Traffic Signs Dataset

Comparative Analysis of Different Machine Learning Techniques on Traffic Signs Dataset | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparative Analysis of Different Machine Learning Techniques on Traffic Signs Dataset Ayyoob. MP, P K Mehthab This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4875838/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The rapid advancements in intelligent transportation systems have highlighted the critical importance of accurate and efficient traffic sign recognition for road safety. Traditional methods relying on handcrafted features and classical machine learning techniques have shown limited accuracy and robustness under varying environmental conditions. This study explores the potential of deep learning, specifically Convolutional Neural Networks (CNNs), for traffic sign recognition. We conduct a comparative analysis of various CNN architectures, including custom models and well-established networks like VGG16, VGG19, EfficientNetB0, and ResNet50. Additionally, we investigate hybrid models that combine features from different architectures to enhance accuracy and robustness. Using the GTSRB dataset, we evaluate the performance of these models based on metrics such as test accuracy, test score, and F1-score. Our findings indicate that custom CNN models and certain hybrid combinations, such as Custom CNN x VGG16, achieve superior performance. This research contributes to the academic understanding of machine learning techniques in traffic sign recognition and holds practical significance for improving the safety and efficiency of modern transportation networks. traffic signs classification convolutional neural network image processing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4875838","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":350407495,"identity":"12402668-6bc7-44f3-be39-84f1579a459d","order_by":0,"name":"Ayyoob. 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