Towards Successful Implementation of Automated Raveling Detection: Effects of Training Data Size, Illumination Difference, and Spatial Shift

ArXi:2604.13322v1 Announce Type: new Raveling, the loss of aggregates, is a major form of asphalt pavement surface distress, especially on highways. While research has shown that machine learning and deep learning-based methods yield promising results for raveling detection by classification on range images, their performance often degrades in large-scale deployments where diverse inference data may originate from different runs, sensors, and environmental conditions. This degradation highlights the need of a generalizable and robust solution for real-world implementation.