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基于机器视觉的胶管尺寸测量系统
Machine Vision based Measurement System of Rubber Hose Size

DOI: 10.12677/JISP.2021.103015, PP. 135-145

Keywords: 机器视觉,尺寸测量,圆检测
Machine Vision
, Dimension Measurement, Circle Detection

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Abstract:

在对胶管进行质量检验时,针对人工使用量具对胶管进行尺寸测量出现的效率低、易受主观影响等问题,本文设计了一种基于机器视觉的胶管尺寸测量系统,实现对胶管内径和壁厚的测量。进行内外圆检测时,使用改进的区域生长法,不同生长梯度方向采用不同顺序扫描;进行内径测量时,由于胶管存在挤压易变形的特殊性质,内径测量误差较大,本文选择测量其内圆面积来判断内径是否达到检验标准;进行壁厚测量时,应用同心圆性质、垂直平分线的性质及勾股定理等原理计算得到各处胶管壁厚。实验结果表明:内径测量误差小于0.07 mm,壁厚测量误差小于0.05 mm,系统响应时间约为2 s,满足工业测量要求,有较好的实用性与应用前景。
In the quality inspection of the hose, for the artificial use of measuring tools to measure the size of the hose appears low efficiency, subject to subjective influence and other problems, this paper designed a machine vision based on the size of the hose measurement system, to achieve the inner diameter and wall thickness of the hose measurement. When the inner and outer circles are detected, the improved region growing method is used, and different growth gradient directions are scanned in different order. When the inner diameter is measured, the inner diameter measurement error is large due to the special property that the rubber hose is easy to be deformed by extrusion. In this paper, the inner circle area is measured to determine whether the inner diameter reaches the inspection standard. When measuring the wall thickness, the concentric circle property, the vertical bisector property and the Pythagorean theorem are used to calculate the wall thickness of various rubber tubes. The experimental results show that the inner diameter measurement error is less than 0.07mm, the wall thickness measurement error is less than 0.05mm, the system response time is about 2s, which meets the requirements of industrial measurement, and has a good practicability and application prospect.

References

[1]  涂德浴, 刘坤, 朱庆, 刘庆运. 基于机器视觉的钢管壁厚在线检测方法研究[J]. 计算机工程与应用, 2021, 卷期号: 1-9.
[2]  伍晶晶, 张士晶, 陈华, 张小海, 冉龙宏, 邬冠华, 高鸿波. 基于CR技术的钢管壁厚测量工艺研究[J]. 失效分析与预防, 2019, 14(5): 300-306.
[3]  丁劲锋, 徐晓佐, 颜东朋, 柴子俊. 基于机器视觉的咖啡胶囊铝箔焊接质量检测[J]. 传感器与微系统, 2021, 40(3): 138-141, 145.
[4]  刘文聪. 视觉机器人在贴合应用中的关键技术研究[D]: [硕士学位论文]. 成都: 电子科技大学, 2014.
[5]  赵博文, 张力夫, 潘在峰, 等. 基于OpenCV的图像滤波方法比较[J]. 信息与电脑(理论版), 2020, 32(15): 78-80.
[6]  Canny, J. (1986) A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8, 679-698.
https://doi.org/10.1109/TPAMI.1986.4767851
[7]  沈夏炯, 段晓宇, 原万里, 等. 基于连通区域标记算法的圆检测算法的研究[J]. 计算机工程与应用, 2018, 54(21): 95-98, 106.
[8]  张平生, 张桂梅. 基于机器视觉的管孔类零件尺寸测量方法[J]. 机械设计与制造, 2012(12): 139-141.
[9]  Wang, K., Lin, W., Dai, F., Liu, Y.Q., Li, J., Qi, X.B. and Lei, H.L. (2021) Annular Phase Filter Based Low Coherence Interferometer for the Profile Measurement of the Transparent ICF Shells. Optics Communications, 491, Article ID: 126959.
https://doi.org/10.1016/j.optcom.2021.126959
[10]  徐万泽, 李柏林, 欧阳, 罗剑桥. 基于环形模板匹配的金属零件识别算法[J]. 传感器与微系统, 2021, 40(2): 128-131.
[11]  宋薇, 陈兴, 沈林勇. 基于视觉的环形密封件在线检测[J]. 仪表技术与传感器, 2020(5): 62-67.

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