Affiliations: [a] Department of Physics and Electronics, North China University of Water Resources and Electric Power, Zhengzhou, Henan | [b] Department of Information Engineering, Zhengzhou Institute of Technology, Zhengzhou, Henan | [c] Collaborative Innovation Center of Water Resources Efficient Utilization and Support Engineering, Zhengzhou, Henan
Correspondence:
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Corresponding author. Ming-Tang Liu, School of Physics and Electronics, North China University of Water Resources and Electric Power, Zhengzhou Henan, China. Tel.: +86 0371 86551378; Fax: +86 0371 65790248; E-mail: [email protected].
Abstract: In order to make the feature data more linearly separable for support vector machine(SVM) classifier, a set of different scale and direction parameter was proposed for improving the recognition effect of cement slope damage in high fill channels. The Gabor wavelet was used to extract the multi-scale and multi-directional features of the high-fill channel’s abrupt features. Then SVM algorithm was utilized to perform damage classification and level recognition. To compare the recognition effect of the Gabor-SVM method, histogram-SVM, grayscale symbiotic matrix -SVM, canny-SVM algorithm were adopted to identify the damage degree of cement surface in the same environment, and these damage recognition rates are compared with Gabor-SVM’s. The experimental results show that the damage recognition model, based on Gabor-SVM, tends to better stable value when the wavelet takes 6 scale and 12 directions. The recognition rate of the normal slope is 0.98, while the recognition rate of the crack, hole, and broken slope are 0.63, 0.88 and 0.90, respectively. Overall, the damage recognition model, based on Gabor-SVM, has better recognition effect, and it will provide technical support for finding potential leakage hazards in the high fill channel of South-to-North Water Diversion Project.
Keywords: Key words: Middle route of South-to-North Water Diversion Project, Feature extraction of multiple
scales and directions, damage recognition, Gabor wavelet, support vector machine