Photogrammetry-Driven Detection of Structural Peeling in Joints and Corners of Rigid Pavements Using an Unsupervised Learning Meta-Architecture

This study proposes the SS-XceVNet meta-architecture, which integrates Xception and VGG16 to detect structural peeling in rigid pavements in an automated manner, thereby reducing the reliance on large, labeled datasets. With increasing traffic and exposure to environmental factors, failures in rigid pavement joints and corners are becoming increasingly frequent, making manual inspections time consuming, dangerous, and subjective. To address this problem, a method was developed in four phases. First, photogrammetry and data augmentation techniques are used to collect and increase the variety of images. Self-supervised learning was then applied to unlabeled data to reduce reliance on manual annotations. SS-XceVNet was then trained on labeled data, combining the robustness of Xception and VGG16 to refine the accuracy. Finally, the results were compared with those of the XceVNet-based model. Evaluations showed that SS-XceVNet achieved 80.43% accuracy, surpassing 47.83% of the base model. This underscores the usefulness of self-supervised learning and integration of advanced architectures in pavement failure detection. In conclusion, this approach improves efficiency in the functional evaluation of road infrastructures while reducing costs and occupational risks, laying the foundation for future research that contributes to the implementation of proactive maintenance systems. Automated detection thus strengthens the quality control of pavements, favoring road safety, and prolonging the useful life of roads.