Detection of zero degree belt loss in radial tire based on multiscale Gabor transform

Xiunan Zheng; Zengzhi Pang; Qingtao Hou; Jinping Li

doi:10.1117/12.2501933

26 July 2018 Detection of zero degree belt loss in radial tire based on multiscale Gabor transform

Xiunan Zheng, Zengzhi Pang, Qingtao Hou, Jinping Li

Proceedings Volume 10828, Third International Workshop on Pattern Recognition; 108280R (2018) https://doi.org/10.1117/12.2501933
Event: Third International Workshop on Pattern Recognition, 2018, Jinan, China

Abstract

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.

Citation Download Citation

Xiunan Zheng, Zengzhi Pang, Qingtao Hou, and Jinping Li "Detection of zero degree belt loss in radial tire based on multiscale Gabor transform", Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 108280R (26 July 2018); https://doi.org/10.1117/12.2501933

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