Pavement Damage Detection

Pavement damage detection research focuses on automating the assessment of road conditions using computer vision, aiming to improve efficiency and accuracy compared to manual inspection. Current efforts center on deep learning models, particularly convolutional neural networks (CNNs) like YOLO variants and UNet architectures, as well as transformer-based models, to detect and classify various types of pavement distress (e.g., cracks, potholes). These advancements offer significant potential for reducing the cost and time associated with road maintenance, enabling more proactive and data-driven infrastructure management. The development of lightweight, real-time capable models is a key area of focus, facilitating applications in automated inspection systems and video surveillance.