Abstract
Tire quality plays an important role in traffic safety. Among the categories of defects that often occur in the actual production process of tires, the tire shoulder belt layer cords joint opening defect is the most common and serious defect. In this paper, a tire shoulder belt layer cords joint opening defect detection algorithm based on grayscale feature statistics and threading method is proposed based on the machine vision nondestructive testing technology. It first combines the periodic texture grayscale feature to accurately pre-locate the defect position, followed by performing a series of pre-processing operations on the target area to accurately determine the tire X-ray image. Through comparative experimental analysis, the detection algorithm has high recognition and accuracy. The detection speed of the algorithm has also reached a satisfactory level.
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Zhang, Z., Peng, C., Rong, M., Xiao, L. (2022). Defect Detection of Tire Shoulder Belt Cord Joint Based on Periodic Texture. In: Fan, W., Zhang, L., Li, N., Song, X. (eds) Methods and Applications for Modeling and Simulation of Complex Systems. AsiaSim 2022. Communications in Computer and Information Science, vol 1712. Springer, Singapore. https://doi.org/10.1007/978-981-19-9198-1_43
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DOI: https://doi.org/10.1007/978-981-19-9198-1_43
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