Surface Defect Detection And Classification Based On Statistical Filter And Decision Tree
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, a...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IJCTE
2013
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/23045/2/794-Z316.pdf http://eprints.utem.edu.my/id/eprint/23045/ http://www.ijcte.org/papers/794-Z316.pdf |
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| Institution: | Universiti Teknikal Malaysia Melaka |
| Language: | English |
| Summary: | 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. |
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