Road Damage Detection

Road damage detection research focuses on automating the identification of pavement defects like cracks and potholes using computer vision techniques, primarily to improve road safety and maintenance efficiency. Current research heavily utilizes deep learning models, particularly object detection architectures such as YOLO and Faster R-CNN, often enhanced with attention mechanisms and data augmentation strategies to address challenges like variations in damage size, appearance, and background complexity. These advancements offer the potential for cost-effective, large-scale road condition monitoring, enabling proactive maintenance and reducing the risk of accidents.