• Sangramsinh Kantilal Bhusnar
• Swacchand Sambhaji Kasar
• Manali Rajendra Mankar
• Sanshi Sharad Hargude
• Prof. P. R. Desai

This desktop program detects damage to roads. Early detection of road deterioration is critical in the field of transportation engineering as it can save maintenance costs and prevent accidents. In recent times, deep learning techniques have shown positive results in several computer vision applications, such as identifying damage to roads. In this investigation, we propose a method for identifying road degradation using a region-based convolutional neural network (R-CNN). We trained our R-CNN on a publicly available collection of road photographs with various types of damage, including cracks, potholes, and patches. Our method identified road damage with an accuracy above 65%, outperforming state-of-the-art techniques.

R-CNN, Project, Road Damage, Machine Learning.

"ROAD DAMAGE DETECTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.j657-j660, April-2024, Available :http://www.jetir.org/papers/JETIR2404981.pdf

"ROAD DAMAGE DETECTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppj657-j660, April-2024, Available at : http://www.jetir.org/papers/JETIR2404981.pdf