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Fast Position and Accurate Segmentation Algorithms for Detecting Surface Defects of the Thermal-State Heavy Rail Based on Machine Vision

Fast Position and Accurate Segmentation Algorithms for Detecting Surface Defects of the Thermal-State Heavy Rail Based on Machine Vision

Xue Wang, Yiran Chen, Tao Cheng, Zhijiang Xie
Copyright: © 2013 |Volume: 5 |Issue: 4 |Pages: 21
ISSN: 1942-9045|EISSN: 1942-9037|EISBN13: 9781466635258|DOI: 10.4018/ijssci.2013100104
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MLA

Wang, Xue, et al. "Fast Position and Accurate Segmentation Algorithms for Detecting Surface Defects of the Thermal-State Heavy Rail Based on Machine Vision." IJSSCI vol.5, no.4 2013: pp.40-60. http://doi.org/10.4018/ijssci.2013100104

APA

Wang, X., Chen, Y., Cheng, T., & Xie, Z. (2013). Fast Position and Accurate Segmentation Algorithms for Detecting Surface Defects of the Thermal-State Heavy Rail Based on Machine Vision. International Journal of Software Science and Computational Intelligence (IJSSCI), 5(4), 40-60. http://doi.org/10.4018/ijssci.2013100104

Chicago

Wang, Xue, et al. "Fast Position and Accurate Segmentation Algorithms for Detecting Surface Defects of the Thermal-State Heavy Rail Based on Machine Vision," International Journal of Software Science and Computational Intelligence (IJSSCI) 5, no.4: 40-60. http://doi.org/10.4018/ijssci.2013100104

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Abstract

Color imaging in the hot rolled condition provides the better reaction of heavy rail on surface defects. In this paper, it proposes a series of novel algorithms of accurate position and segmentation of surface defects of heavy rail. An image acquisition device is designed on the adjustable camera bracket with the linear array CCD, and based on the correlation among pixels at the image level, a fast positioning method is developed for searching the Region Of Interesting (ROI) of the surface defects. Using the Mean-Shift image filtering algorithm which features multi-parameter kernel function, amendments to the sampling point weights and regional search with the nearest neighbor sampling points, accurate segmentation of the identification character is easily achieved by K-means clustering. Experiments show that this algorithm for the identification of the heavy rail surface defects is proven to be more rapid in testing the inclusions, cracks and oxide skin defects with a good promotional value.

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