Study of Highway Crack Diagnosis Based on Cellular Neural Network

Abstract:

Road quality information detect system is an important component in architecture quality detect system, also is the basement of successfully working of other related project for the whole country. The study of detecting the road crack is the key to insure the security of accurately detect the road quality in transportation system. In this paper, we come up with a fixed way of road undersized rift image detection by using cellular neural networks. By image processing, building rift networks and details networks and adding the model of similarity undersized rift networks. It can avoid the problem that can not accurately detect undersized crack by only taking the crack feature value. The experiment proved that fixed crack detect computing is easy to do, more accurate to detect the undersized cracks on the road and can reach the standard level of current detect technique.

• Periodical: Applied Mechanics and Materials (Volumes 427-429)
• Pages: 2013-2017
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.427-429.2013
• Online since: September 2013
• Authors: Sheng Zhuo Yao*, Guo Dong Li, Fu Xin Zhang, Lin Ge
• Keywords: Cellular Neural Network, Crack Diagnosis, Fine Cracks

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