Abstract
Traditional micro-parts defect detection methods are based on human observation to find parts defects, which is inefficient and inaccurate. In order to solve these problems, we designed an automatic parts defect detection system based on machine vision, which include parts transfer module, image capture module, image recognition module and parts selection module. Besides, we put forward a method which is suitable for micro-parts. Experiments show that the method works well and can achieve high accuracy as well as high efficiency, which helps reduce the cost of labor, improve efficiency, production quality and automation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
WORKSHOP R. Workshop on micro/meso-mechanical manufacturing, Evanston, Illinois, USA, Northwestern University (2000)
Li, B.-H., Wang, S.-L., Li, X.-B.: An automatic gauging system for crankshaft. Mechanical Science and Technology for Aerospace Engineering 28(4), 510–513 (2009)
Geng, C.-M., Cai, D.-B.: Design of image measurement system for mechanical parts based on computer vision. Computer Measurement & Control 20(1), 38–40 (2012)
Wei, S., Quan, P., Ying-Le, F., Ping, X.: Automatic automobile parts recognition and classification system based on machine vision. Instrument Technique and Sensor (9), 97–100 (2009)
Jiang, G.-W., Chao, Z.-C., Jiang, H.-P., Fu, S.-H.: Synchronous image acquisition and processing system of multiple cameras based on source trigger and software control. Journal of Applied Optics 30(5), 756–760 (2009)
Wang, Y.-T., Yang, N., Lin, X.-L.: Research on float glass thickness measurement system based on CCD technology. Instrument Technique and Sensor (6), 72–74 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yu, X., Gao, G., He, W., Xu, J. (2013). Machine Vision Based Automatic Micro-parts Detection System. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-42057-3_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42056-6
Online ISBN: 978-3-642-42057-3
eBook Packages: Computer ScienceComputer Science (R0)