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Journal of Sensors
Volume 2016 (2016), Article ID 4140175, 12 pages
http://dx.doi.org/10.1155/2016/4140175
Research Article

Defect Detection in Tire X-Ray Images Using Weighted Texture Dissimilarity

1School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, China
2School of Computer Science and Technology, Shandong University, Jinan 250100, China
3Shandong Provincial Key Laboratory of Digital Media Technology, Jinan 250014, China
4School of Information and Electrical Engineering, Ludong University, Yantai 264025, China

Received 17 September 2015; Accepted 18 February 2016

Academic Editor: Pietro Siciliano

Copyright © 2016 Qiang Guo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Automatic defect detection is an important and challenging problem in industrial quality inspection. This paper proposes an efficient defect detection method for tire quality assurance, which takes advantage of the feature similarity of tire images to capture the anomalies. The proposed detection algorithm mainly consists of three steps. Firstly, the local kernel regression descriptor is exploited to derive a set of feature vectors of an inspected tire image. These feature vectors are used to evaluate the feature dissimilarity of pixels. Next, the texture distortion degree of each pixel is estimated by weighted averaging of the dissimilarity between one pixel and its neighbors, which results in an anomaly map of the inspected image. Finally, the defects are located by segmenting this anomaly map with a simple thresholding process. Different from some existing detection algorithms that fail to work for tire tread images, the proposed detection algorithm works well not only for sidewall images but also for tread images. Experimental results demonstrate that the proposed algorithm can accurately locate the defects of tire images and outperforms the traditional defect detection algorithms in terms of various quantitative metrics.