Research on geometric dimension measurement system of shaft parts based on machine vision

• Published in: EURASIP journal on image and video processing Vol. 2018; no. 1; pp. 1 - 9
• Main Author: Li, Bin
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 03.10.2018; Springer Nature B.V; SpringerOpen
• Subjects: Biometrics; CCD cameras; Computer vision; Dimension measurement; Edge detection; Engineering; Geometric dimension; Image detection; Image Processing and Computer Vision; Machine vision; Measurement methods; Noise reduction; Pattern Recognition; Signal,Image and Speech Processing; Vision systems; Visual Information Learning and Analytics on Cross-Media Big Data; Wavelet analysis; Machine vision; Geometric dimension; Dimension measurement
• ISSN: 1687-5281; 1687-5176; 1687-5281
• DOI: 10.1186/s13640-018-0339-x

Computer vision measurement systems have become more and more widely used in industrial production processes. Traditional manual measurement methods cannot guarantee product quality. Therefore, it is of great significance to improve the technology level of the manufacturing industry to study the automatic measurement system for the dimension of shaft parts with low cost, high precision, and high efficiency. A geometric part measurement system for shaft parts based on machine vision is presented in this paper. It uses the CCD camera to get the image. First, it preprocesses the collected images. In view of the influence of the noise and other factors, the wavelet denoising is used to denoise the image. Then, an improved single pixel edge detection method is proposed based on the Canny detection operator to extract the edge contour of the part image. Finally, the geometrical quantity algorithm is applied to the measurement research, and the measured data are obtained and analyzed. The experimental results show that the repeatability error of the system is less than 0.01 mm.