Research on method of liner inner diameter measurement based on machine vision
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摘要: 衬芯是汽车制动系统中连接刹车油管与制动器的重要零部件，其内径超差会严重影响车辆刹车性能. 针对人工测量衬芯内径尺寸方法效率低、精度差、检测标准不一致等问题，提出基于机器视觉的衬芯内径尺寸测量方法. 利用双边滤波、改进的Otsu阈值法和形态学方法对衬芯内径进行预处理和定位，采用一种改进的Canny边缘检测算子提取内径轮廓点，并基于Tukey权函数实现最小二乘法拟合圆以计算内径尺寸. 试验验证该检测算法的测量误差在 ± 0.01 mm之内，测量准确率为98.9%、漏检率为0、过检率为1.1%，能够满足企业的实际测量要求.Abstract: Liner is an important component connecting brake oil pipe and brake in automobile brake system, and the oversize of inner diameter will seriously affect the brake performance of vehicles. Aiming at the problems of low efficiency, poor accuracy and inconsistent detection standards in the manual measurement of liner inner diameter, a method for measuring liner inner diameter based on machine vision was proposed. The inner diameter of liner was preprocessed and located by using bilateral filtering, improved Otsu threshold method and morphological method. An improved Canny edge detection operator was used to extract the inner diameter contour points, and the least square method was used to fit the circle based on Tukey weight function to calculate the inner diameter size. The experimental verification shows that the measurement error value of the detection algorithm is within ± 0.01 mm, the measurement accuracy is 98.9%, the missing rate is 0, and the passing rate is 1.1%, which can meet the actual measurement requirements of enterprises.
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Key words:
- machine vision /
- image processing /
- dimension detection
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图 14 改进Canny算子与Roberts算子误差图
Figure 14. Error diagram of improved Canny operator and Roberts operator
表 1 标定板的参数
Table 1. Parameters of calibration board
规格/（mm×mm） 类型 阵列 圆心间距/mm 精度/mm 圆大小/mm 20×20 实心圆点 7×7 2.5 0.001 Ф1.25 
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表 2 改进Canny和Roberts算法测量结果比较
Table 2. Comparison of measurement results of improved Canny and Roberts algorithms
批次 检测数量 不合格数 漏检数 过检数 漏检率/% 过检率/% 准确率/% 检测时间/s Ca Ro Ca Ro Ca Ro Ca Ro Ca Ro Ca Ro Ca Ro 1 500 25 51 0 22 6 35 0 4.4 1.2 7.0 98.8 88.6 0.226 0.155 2 500 12 47 0 30 4 24 0 6.0 0.8 4.8 99.2 89.2 0.202 0.189 3 500 17 62 0 21 7 20 0 4.2 1.4 4.0 98.6 91.8 0.241 0.175 总计 1500 54 160 0 63 17 79 0 4.2 1.1 5.3 98.9 90.5 0.223 0.173 注：Ca为改进Canny算法；Ro为Roberts算法.
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