Road Infrastructure Defect Detection using Yolo8Seg Based Approach

This research introduces "Jaddah," an innovative AI-based system for the automated detection of road infrastructure defects using advanced computer vision and machine learning techniques. The system addresses the limitations of traditional road inspection methods, which are often slow and prone to human error. Jaddah develops a mobile application that efficiently detects, classifies, and segments road defects at the pixel level. By utilizing a comprehensive dataset of high-resolution images, the model training process is significantly enhanced. The YOLOv8-seg model is implemented to achieve precise defect localization and segmentation, ensuring high accuracy in identifying and categorizing road defects. Performance metrics show an impressive 87% mAP50, demonstrating reliable defect detection. These results contribute to improved infrastructure maintenance, enhanced road safety, and greater operational efficiency.