Real-Time Pothole Detection Using YOLOv7: An Efficient Deep Learning Approach for Road Safety and Maintenance

Potholes on roadways pose a significant hazard to vehicular safety and infrastructure, particularly in developing regions. Traditional object detection models have shown limitations in either processing speed or adaptability to diverse environmental conditions, hindering their effectiveness in realtime applications. This paper presents a comprehensive evaluation of the YOLOv7 object detection model for automated pothole detection. Leveraging its enhanced architectural design, YOLOv7 demonstrates superior performance in both accuracy and inference speed compared to established models such as Faster R-CNN, SSD, and earlier YOLO versions. Experiments conducted on a dataset encompassing varied road conditions— spanning different lighting, weather, and surface textures—showcase the model's robust generalization capabilities. The results highlight YOLOv7 as a promising solution for integration into real-time intelligent transportation systems and autonomous vehicle platforms, contributing toward safer and more efficient road maintenance strategies.