Authors:
Navid Rabbani
1
;
Mohammad Alamdari
1
;
Mohammad Rohollah Yazdani
2
and
Farhad Imanpour
3
Affiliations:
1
Disdeh Pardaz Saba Co., Iran, Islamic Republic of
;
2
Science & Research Branch, Islamic Azad University (IAU), Iran, Islamic Republic of
;
3
Cold-Rolling Mill II, Isfahan’s Mobarekeh Steel Co., Iran, Islamic Republic of
Keyword(s):
Steel Sheet Defects, Feature Extraction, Feature Selection, SFFS, Computational Complexity, SVM.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Feature Extraction
;
Features Extraction
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image and Video Analysis
;
Informatics in Control, Automation and Robotics
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
Abstract:
This paper presents a novel approach for detection and classification of steel sheet defects. A Defects database with enough samples and good imaging conditions introduced. A set of new features proposed to extract the appropriate textural characteristics from defects images. This is followed by the selection of important features using SFFS algorithm. Modifications to SFFS feature selection method were presented to achieve the real-time needs of research. The proposed scheme decrease computational complexity in cost of little decreasing of classification accuracy.