Machine vision based quality inspection of flat glass products

Gerald Zauner, Martin Schagerl

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung


This application paper presents a machine vision solution for the quality inspection of flat glass products. A contact image sensor (CIS) is used to generate digital images of the glass surfaces. The presented machine vision based quality inspection at the end of the production line aims to classify five different glass defect types. The defect images are usually characterized by very little 'image structure', i.e. homogeneous regions without distinct image texture. Additionally, these defect images usually consist of only a few pixels. At the same time the appearance of certain defect classes can be very diverse (e.g. water drops). We used simple state-of-the-art image features like histogram-based features (std. deviation, curtosis, skewness), geometric features (form factor/elongation, eccentricity, Hu-moments) and texture features (grey level run length matrix, co-occurrence matrix) to extract defect information. The main contribution of this work now lies in the systematic evaluation of various machine learning algorithms to identify appropriate classification approaches for this specific class of images. In this way, the following machine learning algorithms were compared: decision tree (J48), random forest, JRip rules, naive Bayes, Support Vector Machine (multi class), neural network (multilayer perceptron) and k-Nearest Neighbour. We used a representative image database of 2300 defect images and applied cross validation for evaluation purposes.

TitelProceedings of SPIE-IS and T Electronic Imaging - Image Processing
UntertitelMachine Vision Applications VII
Herausgeber (Verlag)SPIE
ISBN (Print)9780819499417
PublikationsstatusVeröffentlicht - 2014
VeranstaltungImage Processing: Machine Vision Applications VII - San Francisco, USA/Vereinigte Staaten
Dauer: 2 Feb. 20146 Feb. 2014


NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (elektronisch)1996-756X


KonferenzImage Processing: Machine Vision Applications VII
Land/GebietUSA/Vereinigte Staaten
OrtSan Francisco


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