Tire Surface Defect Recognition Based on Image Processing

To ensure that the quality of tires meets the requirements and improve production efficiency, it is of great value to use image processing technology to identify defects on the surface of tires. Different types of defects have different recognition algorithms, so we will study dent, crack, slit recognition algorithms, and wear recognition algorithms separately. For dent, crack, and slit defects, defect segmentation and background interference removal are performed to obtain defect areas. Defect recognition is achieved by extracting feature parameters of defects and setting judgment thresholds. For wear defects, the characteristic parameters of wear are calculated through gray level co-occurrence matrix and gray level histogram, and a judgment threshold is set for defect recognition. The experiment shows that the algorithm has a high recognition accuracy for tire defects, with an average accuracy of 96.15%, a fast recognition speed, and a comprehensive average recognition time of about 2.2 seconds. The algorithm has high recognition efficiency.