Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Road Surface Degradation including potholes and cracks causes accidents and make travelling uncomfortable. Manual inspections remain inefficient, costly, and inadequate for expansive networks. This project presents a drone-based road surface monitoring system that uses machine learning to detect potholes, cracks, and other surface defects efficiently. A drone equipped with a high-resolution camera captures continuous video of road segments, and the recorded frames are processed using a YOLO-based deep learning model to automatically identify damaged areas. Each detected defect is geo-tagged using GPS data and stored in a structured dataset for further analysis. Based on the severity and frequency of the identified damages, road segments are classified into different condition levels. A web-based application then analyzes this information to suggest safer and smoother travel routes between selected locations
"Machine Learning-Based Road Surface Assessment And Adaptive Route Suggestion System Using Unmanned Aerial Vehicles And Iot Sensing", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a76-a83, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604010.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator