Tire Impurity Defect Detection Based on Grayscale Correction and Threading Method

Abstract

Impurity defect plays an important role in tire defects, which may directly affect driving safety. A novel model is proposed by this paper for impurity defect detection in tire X-ray images. Firstly, a binarization algorithm based on column grayscale correction is designed, which can obtain more details than other algorithms and lay a solid foundation for the followup detection. Next, a tire X-ray image segmentation algorithm can segment the tire image accurately. Finally, two thresholds are used to judge whether there is an impurity defect in the tire image. The model is evaluated on a real data set which contains various types of impurity defects and achieve good results. But it should be noted that this model can only detect impurity defect in tire body.