Paper Title
YOLO Detect: Intelligent Road Anomaly Detection SYSTEM

Abstract
Road anomalies such as potholes, cracks, and surface irregularities pose serious threats to vehicle safety and passenger comfort. Early detection and classification of these anomalies are crucial for timely maintenance and accident prevention. This study proposes an efficient computer vision-based approach for real-time road anomaly detection using the You Only Look Once (YOLO) object detection algorithm. The model is trained on a labeled dataset of road surface images containing various types of anomalies. It processes video frames or live camera feeds to detect and localize anomalies with bounding boxes. The proposed system demonstrates robust performance in diverse lighting and weather conditions, offering a practical solution for automated road condition monitoring. This approach can be integrated into autonomous vehicles or road inspection systems to enhance transportation infrastructure maintenance