Evaluation of Deep Learning Strategies Using Two Different Image Data Sets for Automated Pavement Distress Detection Methodology

Abstract

This paper investigates strategies to enhance the performance of deep learning models in pavement distress detection by utilizing two distinct datasets collected and annotated from different sources (vendors). The datasets consist of two- and three-dimensional images scanned from asphalt/concrete pavements and manually labeled for multiple surface distresses. Despite using the same base model to train individually, the performance of new models varies between the two datasets for the same pavement type. It remains a question whether transfer learning, knowledge distillation, or data merging will improve weaker models by leveraging stronger ones and increase the models’ robustness across different data sources. Experiments are conducted using these strategies to assess their applicability for single-source data (e.g., from a single vendor) and their generalization for multiple sources (e.g., from two or more vendors). This paper bridges a knowledge gap by qualitatively and quantitatively evaluating the effects of these strategies on current pavement distress detection practices using image data. Optimal approaches, such as fine-tuning or data merging, are recommended for various use cases aimed at real applications.