Research on method of liner inner diameter measurement based on machine vision

FAN Diqing; LI Jinxin; SHA Ling; FANG Yu

doi:10.12299/jsues.22-0368

Volume 37 Issue 1

Mar. 2023

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Article Navigation > Journal of Shanghai University of Engineering Science > 2023 > 37(1): 61-67

FAN Diqing, LI Jinxin, SHA Ling, FANG Yu. Research on method of liner inner diameter measurement based on machine vision[J]. Journal of Shanghai University of Engineering Science, 2023, 37(1): 61-67. doi: 10.12299/jsues.22-0368

Citation:

FAN Diqing, LI Jinxin, SHA Ling, FANG Yu. Research on method of liner inner diameter measurement based on machine vision[J]. Journal of Shanghai University of Engineering Science, 2023, 37(1): 61-67. doi: 10.12299/jsues.22-0368

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Research on method of liner inner diameter measurement based on machine vision

doi: 10.12299/jsues.22-0368

School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Received Date: 2022-12-08
Publish Date: 2023-03-31

Abstract

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.
- machine vision,
- image processing,
- dimension detection

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References(15)

References

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