Defect Inspection for Surfaces with Directional Textures

• Main Authors: Chih-Yun Hsieh, 謝志雲
• Other Authors: Du-Ming Tsai
• Format: Others
• Language: zh-TW
• Online Access: http://ndltd.ncl.edu.tw/handle/45664648670434656145

碩士 === 元智大學 === 工業工程研究所 === 86 === The purpose of this research aims at the use of the machine vision for inspecting defects on surfaces with directional textures. Many surfaces of man-made objects in industry such as machined workparts and textiles can be considered as directional textures. The method of inspecting surface defects is based on two-dimensional Fourier transform (FT) of the surface image and the restoration technique of the inverse Fourier transform (IFT). The Fourier spectrum is ideally suited for describing the directionality of periodic line patterns in a gray-level image. The directional characters of an original image clearly correspond to high-power frequency components that are distributed along straight lines and are orthogonal to original directions in the Fourier spectrum. The lines associated with high-power frequency components in the power spectrum are detected by using the Hough transform. It requires only a simple one-dimensional accumulator to detect the peaks for all possible slope angles of lines in the Fourier spectrum. The frequency components falling on the detected lines or in the neighborhood of the lines are virtually eliminated by setting them to zero in the Fourier domain image. Then an IFT is applied to obtain space domain image. The IFT process will remove all homogeneous, periodic, directional textures in the original gray-level image, and preserves only abnormal features, i.e., defects, if they appear in the surface. Finally, the statistical process control principle is used to set up the control limit for distinguishing defects from noise in the IFT image. Experiments on real directional textures including machined surfaces, textiles and woods have shown promising results using the proposed approach.