An online defects inspection method for float glass fabrication based on machine vision

Quality control is a crucial issue in a float glass factory, and defects existing in float glass can dramatically depress glass grade. Manual inspection in float glass quality control cannot catch up with the development of float glass industry, and automatic glass defect inspection has been a trend. An online defects inspection method for float glass based on machine vision is presented in this paper, and a distributed online defect inspection system for float glass fabrication is realized. This method inspects defects through detecting the change of image gray levels caused by the difference in optic character between glass and defects. A series of image processing algorithms are set up around the analysis of glass image and the requirements of online inspection system such as reliability, real-time, and veracity. Image filtration based on gradient direction is used to filter noise and reserve the source information of defects. Downward threshold based on adaptive surface removes the background composed with stripes and strengthens defect features. Distortion part and core part of defects are obtained through fixed threshold and OTSU algorithms with gray range restricted, respectively. The fake defects (insects, dust, etc.) are eliminated based on the texture of real defects. The application of an inspection system based on this method in Wuhan glass factory proves this inspection method is effective, accurate, and reliable.

[1]  Ajay Kumar,et al.  Inspection of surface defects using optimal FIR filters , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[2]  Jyrki Laitinen.  Dynamic range in automated visual web inspection , 1998 .

[3]  Alfred M. Bruckstein,et al.  A new method for image segmentation , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[4]  Ilya Blayvas,et al.  Efficient computation of adaptive threshold surfaces for image binarization , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[5]  Joan S. Weszka,et al.  A survey of threshold selection techniques , 1978 .

[6]  S. M. Pandit,et al.  Automatic threshold selection based on histogram modes and a discriminant criterion , 1998, Machine Vision and Applications.

[7]  Sankar K. Pal,et al.  A review on image segmentation techniques , 1993, Pattern Recognit..

[8]  Fernando Morgado,et al.  Computer-vision-based gob inspection system for monitoring and control in the glass industry , 2003, IS&T/SPIE Electronic Imaging.

[9]  Michel Jourlin,et al.  Finish inspection by vision for glass production , 1994, Electronic Imaging.

[10]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[11]  Wen-Yong Yu,et al.  Online auto-detection method and system of presswork quality , 2007 .

[12]  P.K Sahoo,et al.  A survey of thresholding techniques , 1988, Comput. Vis. Graph. Image Process..