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Sensors 2015, 15(7), 15326-15338; doi:10.3390/s150715326

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

1
Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Tai-Chung 402, Taiwan
2
Department of Mechatronic Engineering, Huafan University, New Taipei City 223, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 14 May 2015 / Revised: 5 June 2015 / Accepted: 25 June 2015 / Published: 29 June 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [7232 KB, uploaded 29 June 2015]   |  

Abstract

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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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

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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