Light-Emitting Diode Defect Detection based on Support Vector Data Description

• Main Authors: Yen-Chun Chen, 陳衍均
• Other Authors: Yi-Hung Liu
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/34373705695385110602

碩士 === 中原大學 === 機械工程研究所 === 100 === Abstract The Light Emitting Diode (LED) is showing rapid progress in LED’s production line when environmental protection consciousness gains ground .It is an important goal which enhancing the production yield rate of LED’s products for Raising more profit .Therefore, using automatic defects inspection system for LED can reduce human mistake and inspection time, it also can find the problem of machine to avoid LED’s defects. In this research explores the detection of light-emitted area, P-electrode and N-electrode, this system would be inspecting the defect with three mechanisms: Vision Pre-processing, Feature extraction, Training Procedure. Vision Pre-processing is made an adjustment in original defect images, and then to decompose the image to several sub-images. Feature extraction is construction of Discrete Cosine Transform, Texture, Image Power, and Statistics, according to these features, Support Vector Data Description eigenvector is trained with these features. Using to the Support Vector Data Description and the binary image classification to class with LED images.