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

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.

Tire Defects Classification Using Convolution Architecture for Fast Feature Embedding

“…There has been an increasing interest in the use of NDT techniques of defects from steel [1], castings [2], [3], textile [4], TFT-LCD panel [5], nanostructures [6], [7], titanium-coated aluminum surfaces [8], and semiconductors [9] etc. Among these topics, tire defects inspection research is a significant research topic that has been investigated by researchers from both academy and industry areas over the past few decades [10], [11], [12], [13], [14] and is considered as one of the most challenging problems in industrial information revolution era [15] due to its unique properties illustrated in our previous study [11]. Much work has been done on automatic tire defect detection and has been applied in tire X-ray inspection systems to carry out computer vision based automatic defect inspection.…”

Feasibility Study of Motor Powered Agricultural Tractors Based on Physical and Mechanical Properties of Energy Sources

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“…Thus, sufficient attention in design stage is needed, and examining the influence for soil compaction in repeated traveling by the tractor is also required. (Guo et al 2016). This value may fluctuate during a real working condition, however, in this case the value is fixed for a simple calculation.…”

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