Abstract—Industrial quality inspection is a major issue due to
the growing of market competitiveness which requires the
product to be checked in terms of online defect detection.
Meanwhile, labor inspection has been eliminated due to its
limitation that restricts the speed of manufacturing process.
Hence, automated inspection process is inevitable to preserve
the industrial health and lift human function into management
tasks. There are huge efforts on Automated Visual Inspection
(AVI) research area, particularly in plain surfaces such as
ceramics and fabrics. The inspection modeling includes
statistical-based, model-based and color analysis. Most systems
are well studied and tested on Charge-Coupled Device (CCD)
image sensor. However, only few approaches are carried out for
Complementary Metal Oxide Semiconductor (CMOS) imaging
modality. This study presents an inspection scheme to detect
defect in plain fabric based on statistical filter and geometrical
features on CMOS-based image input. The advantage of this
technology is obvious regarding to its affordable development
especially for small and medium industries. We showed that it is
suitable for defect inspection applications that does not require
specialized lighting environment. In addition, a classification
approach is developed based on decision tree framework. The
result for static image shows the classification achieve 99%
accuracy.
Index Terms—Automated visual inspection, plain surfaces,
statistical filter, thresholding, geometrical moments, and
decision tree classifier.
A. Habibullah and Fikri Akbar are with the Faculty of Information and
Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka,
Malaysia (e-mail: habibrown@gmail.com, fikripunya@gmail.com).
Nanna Suryana is with the International Office and lecturer at Faculty of
Information Technology and Communication (FTMK) at Universiti
Teknikal Malaysia Melaka, Melaka, Malaysia (e-mail:
nsuryana@utem.edu.my).
[PDF]
Cite:Habibullah Akbar, Nanna Suryana, and Fikri Akbar, "Surface Defect Detection and Classification Based on
Statistical Filter and Decision Tree," International Journal of Computer Theory and Engineering vol. 5, no. 5, pp. 774-779, 2013.
