Adaptive Universal Pavement Distress Detection Based on Transfer Learning and Deep Learning

Abstract

Various pavements serving in different environments are vulnerable to numerous types of distress. It is crucial to promptly identify and repair fatal pavement distresses. Addressing the issues of time-consuming and labor-intensive traditional pavement distress detection, as well as its lack of precision, an adaptive and universal pavement distress detection method based on deep learning and transfer learning is proposed. First, by summarizing the primary distresses of a large number of different pavements, a classification method based on an improved vision transformer (ViT) deep learning model combined with transfer learning is proposed to categorize these distresses, achieving a Top-1 classification accuracy of more than 97% on four major distress types. Second, more than 3,000 images from multiple public datasets (CrackForest, RDD, CFD, etc.) are integrated using the improved ViT model to construct a universal pavement distress dataset covering local roads, highways, and rural roads. Finally, an improved You Only Look Once v8 (YOLOv8) deep learning model is proposed for multi-information pavement distresses detection. Experimental results demonstrate that while performance on single-pavement datasets is limited by data imbalance, training on the proposed universal pavement distress dataset significantly improves generalization, achieving 82%–89% mean average precision across multiple distress types and enabling robust detection under diverse real-world conditions. Accurate classification and detection of universal pavements have been achieved through the improved ViT and YOLOv8 deep learning models, providing a reference for pavement distress assessment and maintenance.