Defect Detecting Technology Based on Machine Vision of Industrial Parts

In order to reach and test the surface defects on industrial parts, based on Machine Vision this paper put forward a defective parts detection method. The method of median filter was adopted to eliminate the noise of image. The Ostu-method was used for the segmenting threshold. Pixel level and level edge detection were used to complete the precise components defects detection. Experiments show that this scheme is feasible, and can achieve high accuracy and shorter testing time.

[1]  Violet F. Leavers,et al.  The dynamic generalized Hough transform: Its relationship to the probabilistic Hough transforms and an application to the concurrent detection of circles and ellipses , 1992, CVGIP Image Underst..

[2]  A. Asadpour,et al.  Design and application of industrial machine vision systems , 2007 .

[3]  H. Yin,et al.  Image processing techniques for internal texture evaluation of French fries , 2004 .

[4]  Robert L. Stevenson,et al.  Dynamic range improvement through multiple exposures , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[5]  C. Chow,et al.  Automatic boundary detection of the left ventricle from cineangiograms. , 1972, Computers and biomedical research, an international journal.

[6]  Ralph Seulin,et al.  Machine vision system for the inspection of reflective parts in the automotive industry , 2007, Electronic Imaging.

[7]  Owen Robert Mitchell,et al.  Edge Location to Subpixel Values in Digital Imagery , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Zhang Xuewu,et al.  A vision inspection system for the surface defects of strongly reflected metal based on multi-class SVM , 2011 .

[9]  Da-Wen Sun,et al.  Evaluation of the functional properties of Cheddar Cheese using a computer vision method , 2001 .

[10]  Ye Shenghua Research on the Automatic Measuring System Based on Image Measuring Technology , 2007 .

[11]  Xianghua Xie,et al.  A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques , 2008 .

[12]  Chengfeng Li,et al.  Modelling and analysis of micro scale milling considering size effect, micro cutter edge radius and minimum chip thickness , 2008 .