title: A Novel Machine Vision System for the Inspection of Micro-Spray Nozzle.
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%.
authors.affiliation: Department of Bio-Industrial Mechatronics Engineering, National Chung Hsing University, Tai-Chung 402, Taiwan. kuoyi@nchu.edu.tw.
authors.forename: Kuo-Yi
authors.initials: KY
authors.lastname: Huang
authors.affiliation: Department of Mechatronic Engineering, Huafan University, New Taipei City 223, Taiwan. alexyeh1120@hotmail.com.
authors.forename: Yu-Ting
authors.initials: YT
authors.lastname: Ye
mesh_terms: D000465:Algorithms; D004867:Equipment Design; D019544:Equipment Failure Analysis / Q000295:instrumentation* / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D055616:Microtechnology / Q000295:instrumentation; D016571:Neural Networks, Computer
keywords: image processing; micro-spray nozzle; neural network