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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.
Liu et al. (Thu,) studied this question.