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

Qiang Guo,Xiaofeng Zhang,Hui Liu,Caiming Zhang

doi:10.1155/2016/4140175

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.

Highlights

Due to unclean raw materials and undesired manufacturing facilities used in the tire manufacturing process, tire components may be contaminated by various defects, such as metallic or nonmetallic impurities, bubble, and overlap
The performance of the proposed method is evaluated by comparing with image component decomposition (ICD)-based method (ICDM) [15] and the improved waveletbased method (IWaveM) [5]
The scale parameter for ICDM is set to 7, the Daubechies wavelet transform is exploited for IWaveM with four vanishing moments over three decomposition levels, and for the proposed method we utilize a local window with size 5 × 5 and set the patch size 7 × 7, λ = 0.5, τ = 100

Summary

Introduction

Due to unclean raw materials and undesired manufacturing facilities used in the tire manufacturing process, tire components may be contaminated by various defects, such as metallic or nonmetallic impurities (e.g., steel threads, screws, and plastic fragments), bubble, and overlap. In [4], Tajeripour et al proposed a defect detection method which applies the local binary pattern (LBP) to extract texture features. In [15], Guo and Wei proposed a detection method based on the image component decomposition (ICD) technique, which exploits the local total variation filtering and the vertical mean filtering to separate defects from inspected images. Based on the dictionary representation technique, Xiang et al [18] proposed a dictionary based detection method for tire defects by analyzing the distribution of representation coefficients. The reason for the weakness of the above methods is that they do not effectively capture texture distortions of images To address this problem, a simple and efficient detection method is proposed in this paper, which takes advantage of the feature similarity of tire images. As the computational core of the proposed algorithm, computing the feature dissimilarity of image pixels can be implemented independently in parallel, which makes the proposed method feasible for tire online inspection

Characteristics of Tire Images

The Proposed Method

Experiments

Findings

Conclusions