Paper Title
Implementation of an Environment-Specific Roadway Crack Recognition System
Abstract
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.
Keywords - Crack Recognition, Stable Diffusion, Knowledge Distillation
Author - Zikang Wang, Liucun Zhu, Kazuyoshi Yoshino, Shanjun Zhang
Published : Volume-10,Issue-12 ( Dec, 2023 )
DOIONLINE Number - IJAECS-IRAJ-DOIONLINE-20381
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Published on 2024-02-19 |
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