基于高斯拉普拉斯算子的汽车轮胎磨损检测算法

Algorithm for tire wear detection based on Gauss Laplace operator

针对目前人为根据经验判断汽车轮胎是否达到最终使用时间不准确造成资源浪费的问题,提出了一种基于高斯拉普拉斯算子的汽车轮胎磨损检测算法。首先,将拍摄的汽车轮胎图片进行预处理,包括图像灰度化和高斯滤波,用拉普拉斯算子对图像的边缘进行检测;然后,对处理好的图片进行像素坐标提取,通过汽车轮胎花纹深度的检测算法,得到轮胎的实际深度值;最后,将检测的花纹深度值和轮胎使用的临界深度1.6 mm进行对比,若大于这个临界深度,就判断该轮胎可以继续使用,反之,则不可继续使用。实验结果表明:该实验方法能够很好地计算出轮胎花纹的深度值,其准确率可达97.23%以上。

Aiming at the problem of resource waste caused by the inaccuracy of the final use time of automobile tires judged by experience at present, an algorithm for automobile tire wear detection based on Gauss Laplace operator is proposed.Firstly, the image of automobile tire is preprocessed, including image graying and Gaussian filtering, and the edge of the image is detected by Laplace operator.Then the pixel coordinates of the processed image are extracted, and the actual depth of the tire is obtained by the detection algorithm of the tread depth of automobile tire.Finally, the detected tread depth and the critical depth of the tire are 1.6 mm for comparison, if it is greater than the critical depth, it is judged that the tire can continue to use, otherwise, it can’t be used.The experimental results show that this method can calculate the depth of tread pattern very well, and its accuracy can reach over 97.23 %.