• Pratham Rai
• Dr. Ashish Tamrakar
• Khusboo Sao
• MD. Arif Khan

Road and pavement infrastructure conditions are essential to ensure a safe and sound transportation network. Traditional methods of detecting damages of pavement and road are often time-consuming, expensive, and rely heavily on manual inspection. This research paper proposes the utilization of machine learning techniques for the self- operating, labour saving, computerized, automated and accurate detection of pavement and road damage. By leveraging computer vision and image processing algorithms, along with advanced machine learning replica or models, such as(CNN) convolutional neural networks, this study aim to develop a robust system capable of accurately identifying varying types of road and pavement defects. The research will pin point focus on dataset collection, preprocessing, feature extraction, model training, and evaluation to demonstrate the productiveness of the proposed approach in enhancing road infrastructure maintenance practices.

1.Automated infrastructure monitoring, 2. Computer vision for road improvement , 3. Machine learning in pavement inspection

"", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.q77-q83, April-2024, Available :http://www.jetir.org/papers/JETIR2404H10.pdf

"", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppq77-q83, April-2024, Available at : http://www.jetir.org/papers/JETIR2404H10.pdf