Development of a roadway crack detection system tailored for specific environments

Earthquake is one of the natural disasters that Japan often suffers from, and it may cause cracks and other damages on roads, which seriously affects transportation safety and economic development. This study aims to develop a roadway crack recognition system to cope with road damage after earthquakes in a specific environment. The system first uses the YOLOv5 algorithm to identify cracks on the road, then intercepts the parts of the road identified as "cracks" and uses the U-Net algorithm to perform semantic segmentation. Finally, the area of the semantically segmented crack area is calculated to evaluate the damage degree of the crack. We use the Stable Diffusion algorithm to generate a context-specific crack dataset. Furthermore, in order for the model to perform well on camera devices, we use knowledge distillation to compress the trained model. Experimental results show that the system performs well in terms of crack detection accuracy and speed, and is able to quickly and accurately identify and assess cracks on roads. The development of this system is important for improving the efficiency of road crack monitoring and is expected to be widely used in road maintenance after earthquake disasters in the future.