Road Damage

Road damage detection is a crucial area of research aiming to improve road safety and infrastructure maintenance efficiency. Current efforts heavily utilize deep learning, particularly object detection models like YOLO and Faster R-CNN, often enhanced with attention mechanisms and novel loss functions to improve accuracy and speed, even on resource-constrained devices. These advancements are driven by the need for automated, real-time damage assessment, leveraging datasets from various sources including crowdsourced images and Google Street View, and leading to improved algorithms for identifying diverse damage types, including cracks, potholes, and damaged signage. The resulting technologies offer significant potential for optimizing resource allocation and enhancing road safety through proactive maintenance strategies.