Tunnel crack detection and classification system based on image processing

Zhiwei Liu; Shahrel A Suandi; Takeshi Ohashi; Toshiaki Ejima

doi:10.1117/12.460191

8 March 2002 Tunnel crack detection and classification system based on image processing

Zhiwei Liu, Shahrel A Suandi, Takeshi Ohashi, Toshiaki Ejima

Proceedings Volume 4664, Machine Vision Applications in Industrial Inspection X; (2002) https://doi.org/10.1117/12.460191
Event: Electronic Imaging, 2002, San Jose, California, United States

Abstract

In this paper, an efficient tunnel crack detection and recognition method is proposed. It combines the analysis of crack intensity feature and the application of Support Vector Machine algorithm. At first, the original image is transformed into a binary image. Based on two thresholds technique, the object edge image can be obtained. Then assuming the image can be separated to some local images, we catagorize the local image into three types of pattern. They are the crack, non-crack and intermediate type, which have both of the two properties. A trainable classifier is built to classify these patterns. During this process, Balanced sub-images that satisfy for the two centers of geometric and gravity, are used as a trainable sample for the classifier. This leads to an effective classification system.

Citation Download Citation

Zhiwei Liu, Shahrel A Suandi, Takeshi Ohashi, and Toshiaki Ejima "Tunnel crack detection and classification system based on image processing", Proc. SPIE 4664, Machine Vision Applications in Industrial Inspection X, (8 March 2002); https://doi.org/10.1117/12.460191

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