Crack segmentation in roads using synthetic data and RGB-D data fusion

In this paper, we use deep learning on the task of crack segmentation using a novel data fusion approach with RGB-D data. We use an existing architecture with DeepLabV3 and synthetic data to address the issue of limited availability for real-world data. The synthetic data is generated with Blender and BlenSor to accurately model the real-world crack scenarios. We train the model with a mixture of real-world data and synthetic data and evaluate it on a real-world dataset. The results show significant improvements over baseline models that only use the RGB data when evaluated with the IoU and F1-score. This demonstrates the success of using synthetic data for crack segmentation with data fusion and suggests a promising direction for future crack detection research to provide increased accuracy in automated maintenance and monitoring applications.