Pavement Condition

Pavement condition assessment focuses on accurately evaluating the state of road surfaces to inform efficient maintenance and improve infrastructure longevity. Current research heavily utilizes deep learning, employing architectures like YOLO, convolutional neural networks, and transformers, to automate tasks such as distress detection (e.g., cracks, potholes), segmentation, and Pavement Condition Index (PCI) prediction from images and sensor data. These advancements enable faster, more objective assessments, leading to optimized maintenance strategies and improved road safety, particularly beneficial in resource-constrained settings. The development of large, diverse datasets is also crucial for training and validating these advanced models, improving their generalizability and robustness.