Abstract—Nowadays, the increasing population increases the
consumption as well. Manufacturing systems are developing in
a fast pace to meet increasing demand of consumption. However,
very quick increase of production has surpassed the
development speed of currently existing control systems. In
manufacturing, since the quality is a very important issue as
well as the quantity, the operation of quality control systems
must be accelerated and must be accomplished by machines.
The idea of our study is based on this thinking. Especially defect
detection on widely used glass material that is extremely
difficult to accomplish by humans can be implemented in a
quick, accurate and stable way. In the presented method,
various defects like scratches, bubbles, cracks, corrosion on the
glass surface can be identified. Glass images obtained from a
homogenously illuminated medium are processed by wavelet
transform and obtained images from the wavelet transform are
denoised. Finally, Shannon threshold method is applied to the
images. From the obtained results, defects like scratches,
bubbles on the surface of the glass can be detected successfully.
Index Terms—Glass defect detection, texture analysis, image
processing, wavelet transform, machine vision.
The authors are with the Electrical and Electronics Engineering
Department, Engineering Faculty, University of Selcuk, Konya, Turkey
(e-mail: {bayakdemir, sabanozturk}@selcuk.edu.tr).
[PDF]
Cite: Bayram Akdemir and Şaban Öztürk, "Glass Surface Defects Detection with Wavelet Transforms," International Journal of Materials, Mechanics and Manufacturing vol. 3, no. 3, pp. 170-173, 2015.
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