Automated detection of pavement distress based on enhanced YOLOv8 and synthetic data with textured background modeling

Regular inspections of pavement distress are essential for accident prevention, and deep learning based algorithms have been developed to ensure high accuracy of inspections. However, the lack of available data is a critical challenge for existing algorithms. To address this issue, an elaborate image synthesis strategy is proposed. By combining textured background modeling for real pavement and Unreal Engine-based distress block stitching technique, high-resolution virtual images that are indistinguishable from real images are generated, including five types of pavement distresses. In addition, an enhanced YOLOv8 network utilizing synthetic data is designed in this paper. The enhanced YOLOv8 network is embedded with the Squeeze and Excitation attention module, and the Swin Transformer module, which are designed to distinguish different types of pavement distress accurately and suppress the interference of complex backgrounds (noise, shadow, blur). The results show that the performance of the algorithms trained based on appropriately ratios of synthetic data (2:1 ratio of virtual to real) improves by more than 10% compared with no virtual data. The enhanced YOLOv8 network achieves a Mean Average Precision (MAP) of 94.8% for transverse cracks, longitudinal cracks, cross-cracks, alligator cracks, and potholes, which is better than seven existing object detection models. The proposed image synthesis method can contribute to improving the accuracy and reliability of pavement inspection and alleviate the reliance on large number of distress samples collection.