• DASU DASARI
• D.N.V.S.Vijaya Lakshmi
• Tatapudi Ashok

This research presents an innovative approach to the automated detection of road damage using deep learning, emphasizing the efficiency and accuracy of YOLOv8, a cutting-edge object detection algorithm. Maintaining road infrastructure is crucial for safe and efficient transportation, yet manual inspections are time-consuming and hazardous. By leveraging high-resolution image data and the YOLOv8 model, the proposed system enables real-time detection and classification of various road damages, including cracks and potholes. The methodology involves training the YOLOv8 model on a diverse dataset, ensuring its ability to recognize distinct damage patterns with high precision. The results demonstrate the potential of this automated system to enhance road maintenance processes, offering a cost-effective and scalable solution for safer transportation networks.

Road damage detection, YOLOv8, deep learning, automated inspection, object detection, transportation safety.

"Real-Time Detection of Road Damages Using YOLOv8: An Innovative Deep Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.h459-h465, December-2024, Available :http://www.jetir.org/papers/JETIR2412755.pdf

"Real-Time Detection of Road Damages Using YOLOv8: An Innovative Deep Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. pph459-h465, December-2024, Available at : http://www.jetir.org/papers/JETIR2412755.pdf