Investigation of the Road Surface Defects Using Robust Techniques with Multi-Sensor Integration

Investigation of the Road Surface Defects Using Robust Techniques with Multi-Sensor Integration | 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 Investigation of the Road Surface Defects Using Robust Techniques with Multi-Sensor Integration Muhammad Shahzad Alam Khan, Hammad Majeed, Anas Bin Aqeel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9282917/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Road surface identification is crucial for enhancing the transportation system since developing new roads in poor countries is extremely challenging due to financial problems. Hence, it is more important to repair existing roads than to build brand-new ones. At the moment, manual inspections are used to find damaged roads, which is very expensive. A robust embedded device is required to identify rough road surfaces utilizing a multi-sensor. A few methodologies are examined in this study to determine surface roughness and categorize various road faults. The first approach uses autocorrelation and linear prediction coding (LPC) to calculate the road's roughness index (RI) using a filter-based strategy. The second Artificial Intelligence (AI) approach uses a common random forest (RF) approach to classify defects, including potholes and cracks. In the third strategy, the heatmap-based digital image processing (DIP) technique is used to classify road roughness into more precise categories, including potholes, rough patches, longitudinal, alligator, and transverse cracks. The YOLO model is used to validate defect detection results using a monocular camera, which shows excellent validation accuracy across all detection methods. The filter-based technique obtains an accuracy of 84.21% following YOLO validation. F1 scores of 78.4% for potholes and 83.9% for cracks are obtained using AI (RF) approach. After using YOLO for validation, the heatmap approach achieved 90.3%. The technology integrates geo-localization using GPS to determine the position of each problem, assisting in road maintenance. Road Surface Multi-Sensor Artificial Intelligence Geo-localization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 05 Apr, 2026 Editor assigned by journal 02 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 31 Mar, 2026 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. 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