Affiliation:
1. Institute of Industrial Science The University of Tokyo Tokyo Japan
Abstract
AbstractRoad cracks are a major concern for administrators. Visual inspection is labor‐intensive. The accuracy of previous algorithms for detecting cracks in images requires improvement. Further, the length and thickness of cracks must be estimated. Light detection and ranging (Lidar), a standard smartphone feature is used to develop a method for the completely automatic, accurate, and quantitative evaluation of road cracks. The two contributions of this study are as follows. To achieve the highest segmentation accuracy, U‐Net is combined with data augmentation and morphology transform. To calculate the crack length and thickness, crack images are registered into Lidar color data. The proposed algorithm was validated using a public database of road cracks and those measured by the authors. The algorithm was 95% accurate in determining crack length. The coefficient of determination for thickness estimation accuracy was 0.98 addressing various crack shapes and asphalt pavement patterns.
Subject
Computational Theory and Mathematics,Computer Graphics and Computer-Aided Design,Computer Science Applications,Civil and Structural Engineering,Building and Construction
Reference84 articles.
1. 3D Scanner App. (2022).https://3dscannerapp.com
2. A dynamic ensemble learning algorithm for neural networks
3. American Society of Civil Engineers (ASCE). (2021).Infrastructure report card.https://infrastructurereportcard.org/cat‐item/roads‐infrastructure
4. Apple Inc. (2020).iPhone 12 Pro technical specifications.https://support.apple.com/kb/SP831?viewlocale=en_US&locale=en_US
5. Encoder–decoder network for pixel‐level road crack detection in black‐box images
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献