| 程良利,唐旭晟,朱博文,陈 丹.基于机器视觉的水表机芯灵敏度检测[J].电子测量与仪器学报,2021,35(6):88-95 |
| 基于机器视觉的水表机芯灵敏度检测 |
| Sensitivity detection of water meter movement based on machine vision |
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| DOI： |
| 中文关键词: 机器视觉 水表检定 灵敏度 角点检测 |
| 英文关键词:machine vision water meter calibration sensitivity corner detection |
| 基金项目:福建省自然科学基金（2018J01534）资助项目 |
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| 摘要点击次数: 873 |
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| 中文摘要: |
| 为了解决人工检测水表机芯灵敏度存在效率低、精度差等问题,开发了一套基于机器视觉的水表机芯灵敏度检测系统。
设计了一种基于指针通气前后转动角度差的间接检测算法。 利用最小二乘法求得初始轮廓圆心,以圆心的横坐标和纵坐标将
指针轮廓分为 4 部分,计算每部分轮廓点到圆心距离的标准差,取标准差值最小部分作为轮廓集合来拟合出精确圆,实现指针
圆心定位;结合 Shi-Tomas 角点检测算法和针尖点到圆心的距离特征实现针尖点定位;计算通气前后各子表盘指针圆心与针尖
点所形成直线与水平线之间的夹角,将角度差换算的脉冲数与设定脉冲数阈值比对,判断是否合格。 实验表明,该系统在保持
检测准确性的前提下能够高效的完成水表机芯灵敏度检测,算法的检定正确率高达 99. 7%。 |
| 英文摘要: |
| In order to solve the problems of low efficiency and poor accuracy in manual detection of water meter movement sensitivity, a
set of water meter movement sensitivity detection system based on machine vision was developed. Designing an indirect detection
algorithm based on the difference in the rotation angle of the pointer before and after ventilation. Using the least square method to find the
center of the initial contour, dividing the pointer contour into four parts with the abscissa and ordinate of the center, calculating the
standard deviation of the distance from the contour point of each part to the center of the circle and taking the smallest part of the
standard deviation as the contour set to fit draw a precise circle, realizing the pointer center positioning, combining the Shi-Tomas corner
detection algorithm and the distance feature from the needle point to the center of the circle to realize the needle point positioning;
calculating the angle between the straight line and the horizontal line formed by the center of each sub-dial pointer and the needle point
before and after ventilation, comparing the pulse number converted from the angle difference with the set pulse number threshold, and
judging whether it is qualified. Experiments show that the system can efficiently complete the sensitivity detection of water meter
movement while maintaining the accuracy of detection, and the accuracy of algorithm verification is as high as 99. 7%. |
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