G.Swathi Reddy1, Maidamshetti Ajay2, Palagani Anil3, Chowdari Eshwar4, Bhukya Somesh5, Alugulla Vamshi Kiran6 03 06 This study introduces an AIdriven system for detecting and localizing road surface damage using deep learning methods The approach employs three variants of the YOLO You Only Look Once architectureYOLOv5 YOLOv7 and YOLOv8to analyze and classify road images A custom dataset containing multiple categories of road defects including potholes cracks and surface irregularities was used for training and evaluation Comparative results indicate that YOLOv8 delivers the best performance achieving an accuracy of 85 followed by YOLOv7 at 82 and YOLOv5 at 65 Additional evaluation metrics such as precision recall and confusion matrices further confirm the superior detection capability of YOLOv8 with reduced misclassification rates The system also supports realtime detection visually marking damaged areas with bounding boxes Overall the proposed framework demonstrates the effectiveness of deep learning in automating road condition assessment with practical applications in smart infrastructure management and autonomous transportation systems Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026