ISSN 0253-2778

CN 34-1054/N

Open AccessOpen Access JUSTC

Tire impurity defect detection based on morphology and projection histogram

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2019.01.007
  • Received Date: 19 June 2018
  • Rev Recd Date: 18 September 2018
  • Publish Date: 31 January 2019
  • A method based on morphology and projection histogram is proposed, which aims at detecting impurities in the sidewall and shoulder of the tire. Firstly, image preprocessing is used to segment the sidewall and shoulder, decreasing the negative influence caused by the tread pattern. Secondly, the binarized images are obtained by using OTSU in the local area, while impurities are extracted from the background by morphological operation. Then, vertical filtering is performed to remove burrs. Finally, the impurity is located by vertical and horizontal projections. The position of the impurity is random, and the projection curves of impurities are consistent with the square wave model, so the impurities are filtered according to the above characteristics of impurity. The experiments show that the proposed method can effectively detect impurities in the sidewall and shoulder outside the tread pattern, and that it can also meet the system requirement of real-time performance.
    A method based on morphology and projection histogram is proposed, which aims at detecting impurities in the sidewall and shoulder of the tire. Firstly, image preprocessing is used to segment the sidewall and shoulder, decreasing the negative influence caused by the tread pattern. Secondly, the binarized images are obtained by using OTSU in the local area, while impurities are extracted from the background by morphological operation. Then, vertical filtering is performed to remove burrs. Finally, the impurity is located by vertical and horizontal projections. The position of the impurity is random, and the projection curves of impurities are consistent with the square wave model, so the impurities are filtered according to the above characteristics of impurity. The experiments show that the proposed method can effectively detect impurities in the sidewall and shoulder outside the tread pattern, and that it can also meet the system requirement of real-time performance.
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