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Open AccessArticle

High-Precision Detection of Defects of Tire Texture Through X-ray Imaging Based on Local Inverse Difference Moment Features

by Guo Zhao * and Shiyin Qin *
School of Automation Science and Electrical Engineering, Beihang University, Haidian District, Beijing 100191, China
*
Authors to whom correspondence should be addressed.
Sensors 2018, 18(8), 2524; https://doi.org/10.3390/s18082524
Received: 28 June 2018 / Revised: 27 July 2018 / Accepted: 28 July 2018 / Published: 2 August 2018
(This article belongs to the Special Issue Sensors Signal Processing and Visual Computing)
Automatic defect detection is an important and challenging issue in the tire industrial quality control. As is well known, the production quality of tire is directly related to the vehicle running safety and passenger security. However, it is difficult to inspect the inner structure of tire on the surface. This paper proposes a high-precision detection of defects of tire texture image obtained by X-ray image sensor for tire non-destructive inspection. In this paper, the feature distribution generated by local inverse difference moment (LIDM) features is proposed to be an effective representation of tire X-ray texture image. Further, the defect feature map (DFM) may be constructed by computing the Hausdorff distance between the LIDM feature distributions of original tire image and each sliding image patch. Moreover, DFM may be enhanced to improve the robustness of defect detection algorithm by a background suppression. Finally, an effective defect detection algorithm is proposed to achieve the pixel-level detection of defects with high precision over the enhanced DFM. In addition, the defect detection algorithm is not only robust to the noise in the background, but also has a more powerful capability of handling different shapes of defects. To validate the performance of our proposed method, two kinds of experiments about the defect feature map and defect detection are conducted to demonstrate its good performance. Moreover, a series of comparative analyses demonstrate that the proposed algorithm can accurately detect the defects and outperforms other algorithms in terms of various quantitative metrics. View Full-Text
Keywords: non-destructive testing; digital X-ray image sensor; defect feature map; defect detection; gray level co-occurrence matrix; local inverse difference moment non-destructive testing; digital X-ray image sensor; defect feature map; defect detection; gray level co-occurrence matrix; local inverse difference moment
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Zhao, G.; Qin, S. High-Precision Detection of Defects of Tire Texture Through X-ray Imaging Based on Local Inverse Difference Moment Features. Sensors 2018, 18, 2524.

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