Research On Tire Defect Detection Based On Machine Vision

With the rapid development of the automobile industry,the safety performance of the automobile is closely watched,and the quality of automobile tires is a focus of people’s attention,which is directly related to people’s life safety.At present,the tire defect detection mainly relies on manual detection,which is high labor cost,and the detection standard is subject to subjective influences,which seriously affects the production efficiency of the product.In recent years,machine vision detection technology has been developed rapidly,and its stability,high efficiency and automation have laid a theoretical foundation for the tire defect detection system.In this paper,machine vision detection technology is used to study the defects of tire parts.The main work of this paper are as follows:(1)The first is to build the system hardware platform.The research and analysis of the defect characteristics of the inner,side and seam allowance of the tire,through several experiments,determine the two modules that make up the hardware platform,namely,the mechanical rotation module and the image acquisition module.(2)Secondly,it studies and realizes the defect detection algorithm for each part of the tire.According to the analysis of the images collected by the hardware platform,three different defect detection algorithms are designed according to different types of defects.The first is inside the tire,there are uneven defects.After analysis,the image is processed by the method of Laws texture filtering.By using gaussian mixture classifier to classify images and classify them by training classifier,the purpose of extracting defects can be realized.The second is in the Seam allowance of the tire,the main defect of the tire is the outlet edge of the tire,which is analyzed and studied to extract the edge defect from the same method as the inner defect of the tire.In contrast,the method of local texture extraction is used to detect the Seam allowance defect of tire.The Seam allowance is located in the image,so when classifying the Seam allowance,the defect is extracted.The third one is on the tire side,the main defects are lack of glue and scratches.Through the analysis and research,the image was detected by threshold segmentation,open and closed operation and feature screening,and the defect of the sidewall as extracted finally.(3)Finally,the implementation of the cross section detection.By analyzing the characteristics of the research section structure,adopting the method based on edge detection to extract the section contour,segmenting the contour,detecting the position point coordinates,and finally obtaining the distance of the position of test point to the contour is the thickness of the location point.According to the detection system designed in this paper,the test samples are verified by experiments.The results show that: the accuracy rate of internal defect detection is 99.5%,the accuracy rate of the Seam allowance of the tire defect detection is 100%,the accuracy rate of the sidewall defect detection is 99%,and the accuracy of the cross section detection is 100%,which can realize the cross section detection of the tire,the system has practical application value.