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Surface Defect Detection of HGA Using Wavelet Method

Author: ZhuZhiLong
Tutor: WangZuo；HuangWei
School: Harbin Institute of Technology
Course: Control Science and Engineering
Keywords: Defect Detection Wavelet decomposition Texture Modeling Sui Airport Surface defects
CLC: TP391.41
Type: Master's thesis
Year: 2008
Downloads: 26
Quote: 1
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Abstract


Machine vision technology has achieved rapid development in recent years , the future will be widely used in industrial machine vision product defect detection , the paper will examine the use of machine vision instead of human eyes for head flap detection of defects such as surface scratches . Head is a computer hard drive for data reading portion of the head is the head flaps play a supportive role on a steel bracket. Head flap surface indentations , scratches and other defects that affect product aerodynamics , will also affect the appearance of the affected customer's impression. Under the microscope, there is a uniform steel surface natural texture , when the textured surface defects , it will be destroyed natural texture . We need to distinguish between natural texture background defect area , and we propose a texture at different scales for the modeling methodology , hoping to detect defects in the product area . We built an experimental platform, including the brackets, fixed products, fixtures , lighting, camera and image processing computer. We first wavelet decomposition for texture images obtained at different scales and approximate image detail images , so you can make our analysis on different scales texture. More in line with the nature of the texture . Can also learn to find the law . Because of this characteristic texture closer to the airport with the requirement that the value of each point on the image only relationship with its surrounding pixels Jiaotong University, so we used with the airport right texture modeling methods, namely the wavelet decomposition texture modeling , image selection criteria extract modeled texture parameters, when the defect is detected, the test image and the standard image parameters for comparison. If you exceed a certain range is considered substandard images. The results achieved the expected goals, most defects can be detected image .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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