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Open AccessArticle

A Novel Machine Vision System for the Inspection of Micro-Spray Nozzle

by Kuo-Yi Huang 1,* and Yu-Ting Ye 2
Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Tai-Chung 402, Taiwan
Department of Mechatronic Engineering, Huafan University, New Taipei City 223, Taiwan
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Sensors 2015, 15(7), 15326-15338;
Received: 14 May 2015 / Revised: 5 June 2015 / Accepted: 25 June 2015 / Published: 29 June 2015
(This article belongs to the Section Physical Sensors)
In this study, we present an application of neural network and image processing techniques for detecting the defects of an internal micro-spray nozzle. The defect regions were segmented by Canny edge detection, a randomized algorithm for detecting circles and a circle inspection (CI) algorithm. The gray level co-occurrence matrix (GLCM) was further used to evaluate the texture features of the segmented region. These texture features (contrast, entropy, energy), color features (mean and variance of gray level) and geometric features (distance variance, mean diameter and diameter ratio) were used in the classification procedures. A back-propagation neural network classifier was employed to detect the defects of micro-spray nozzles. The methodology presented herein effectively works for detecting micro-spray nozzle defects to an accuracy of 90.71%. View Full-Text
Keywords: micro-spray nozzle; image processing; neural network micro-spray nozzle; image processing; neural network
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Huang, K.-Y.; Ye, Y.-T. A Novel Machine Vision System for the Inspection of Micro-Spray Nozzle. Sensors 2015, 15, 15326-15338.

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