An improved segmentation method for defects inspection on steel roller surface

In the field of metal rolling, the quality of the steel roller’s surface is significant for the final rolling products, e.g. metal sheets or foils. Besides the dimensional accuracy and surface roughness, the optical uniformity of the roller surface is also required for high quality rolling application. The typical optical defects of rollers after finish grinding include speckles, chatter marks, feed traces, and combination of all above. Unlike surface roughness, the optical defects can hardly be characterized by the topography or scanning electrical microscope measurement. Only the inspection by bared eyes of experienced engineers appears to be the effective manner for surface optical defects examination for large steel rollers. In this paper, an on-site machine vision system is designed to add on to the roller grinding machine to capture the surface image, and then an improved optical defects segmentation algorithm is developed based on the active contour model. Finally, experiments are carried out to verify the efficacy of the improved model.In the field of metal rolling, the quality of the steel roller’s surface is significant for the final rolling products, e.g. metal sheets or foils. Besides the dimensional accuracy and surface roughness, the optical uniformity of the roller surface is also required for high quality rolling application. The typical optical defects of rollers after finish grinding include speckles, chatter marks, feed traces, and combination of all above. Unlike surface roughness, the optical defects can hardly be characterized by the topography or scanning electrical microscope measurement. Only the inspection by bared eyes of experienced engineers appears to be the effective manner for surface optical defects examination for large steel rollers. In this paper, an on-site machine vision system is designed to add on to the roller grinding machine to capture the surface image, and then an improved optical defects segmentation algorithm is developed based on the active contour model. Finally, experiments are carried out to ve...

[1]  V. Caselles,et al.  A geometric model for active contours in image processing , 1993 .

[2]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[3]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[4]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[5]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[6]  W. Clem Karl,et al.  A fast level set method without solving PDEs [image segmentation applications] , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[7]  Mila Nikolova,et al.  Algorithms for Finding Global Minimizers of Image Segmentation and Denoising Models , 2006, SIAM J. Appl. Math..

[8]  Xavier Bresson,et al.  Fast Global Minimization of the Active Contour/Snake Model , 2007, Journal of Mathematical Imaging and Vision.

[9]  Chunming Li,et al.  Implicit Active Contours Driven by Local Binary Fitting Energy , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Adel Said Elmaghraby,et al.  A graph cut based active contour without edges with relaxed homogeneity constraint , 2008, 2008 19th International Conference on Pattern Recognition.

[11]  Lei Zhang,et al.  Active contours driven by local image fitting energy , 2010, Pattern Recognit..

[12]  Emmanuel Viennet,et al.  A Convex Active Contour Region-Based Model for Image Segmentation , 2011, CAIP.

[13]  Linfang Xiao,et al.  Active contours driven by region-scalable fitting and optimized Laplacian of Gaussian energy for image segmentation , 2017, Signal Process..