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Detection of Zero Degree Belt Loss in Radial Tire Based on Multiscale Gabor Transform

机译:基于多尺度Gabor变换的子午线轮胎零度带损检测

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摘要

Tire safety is becoming more and more important with the increasing number of vehicles. The Zero Degree Belt Loss (ZDBL) is one of the important defects in radial tire that attract serious attention, which can result in fatal influence on the tire quality. In this study, an effective detection method to detect ZDBL in all steel radial tire based on multiscale Gabor transform and morphological filter is proposed. First of all, the multiscale and multi direction Gabor filtering of the tire tread image is carried out. After Gabor filtering, it was found that the texture of the 0 degree belt is obviously different from the other parts in zero degree direction. Then, according to the direction feature extracted by the Gabor transform, a morphologic filter is constructed to remain zero degree direction texture. Finally, if the pixel number is less than threshold in 0 degree direction of the tire tread after morphological filtering, the tire can be judged with ZDBL. 800 tire images are used in our experiment. These images are obtained from a tire factory, which including 100 normal images without any defects, 100 images with ZDBL and 600 images with other types of defects. The results show that the precision is 99.8% and the recall rate can reach 99.9%. Testing in the tire factory have also achieved good results without misreporting.
机译:随着车辆数量的增加,轮胎安全性变得越来越重要。零度带损(ZDBL)是子午线轮胎中引起人们严重关注的重要缺陷之一,它可能对轮胎质量造成致命影响。提出了一种基于多尺度Gabor变换和形态学滤波的全钢子午线轮胎中ZDBL的有效检测方法。首先,对轮胎胎面图像进行多尺度和多方向Gabor滤波。经过Gabor滤波后,发现0度带的织构在零度方向上明显不同于其他部位。然后,根据通过Gabor变换提取的方向特征,构造一个形态过滤器以保持零度方向纹理。最后,如果经过形态学滤波后在轮胎胎面0度方向上的像素数小于阈值,则可以用ZDBL判断轮胎。我们的实验中使用了800张轮胎图像。这些图像是从轮胎工厂获得的,其中包括100张没有任何缺陷的普通图像,100张具有ZDBL的图像和600张具有其他类型缺陷的图像。结果表明,该方法的准确率为99.8%,召回率可以达到99.9%。在轮胎工厂进行的测试也取得了不错的成绩,而且没有误报。

著录项

  • 来源
    《Third International Workshop on Pattern Recognition》|2018年|108280R.1-108280R.10|共10页
  • 会议地点 Jinan(CN)
  • 作者单位

    School of Information Science and Engineering, University of Jinan, Jinan, Shandong 250022, China,Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan Shandong 250022, China,Shandong College and University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-year, University of Jinan, Jinan 250022, China;

    School of Information Science and Engineering, University of Jinan, Jinan, Shandong 250022, China,Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan Shandong 250022, China,Shandong College and University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-year, University of Jinan, Jinan 250022, China;

    Shandong Shentu Intelligent Technology Co., Ltd., Jinan, Shandong, 250200, China;

    School of Information Science and Engineering, University of Jinan, Jinan, Shandong 250022, China,Shandong Provincial Key Laboratory of Network Based Intelligent Computing, University of Jinan, Jinan Shandong 250022, China,Shandong College and University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-year, University of Jinan, Jinan 250022, China;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Tire tread defect detection; zero degree belt loss; multiscale Gabor transform; morphological filter;

    机译:轮胎胎面缺陷检测;零度皮带损失;多尺度Gabor变换形态过滤器;

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