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Metals 2018, 8(3), 197; https://doi.org/10.3390/met8030197

Online Surface Defect Identification of Cold Rolled Strips Based on Local Binary Pattern and Extreme Learning Machine

1
,
1,* and 2
1
Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China
2
Quantitative Imaging Research Team, Data61, Commonwealth Scientific and Industrial Research Organization, Marsfield, NSW 2122, Australia
*
Author to whom correspondence should be addressed.
Received: 12 February 2018 / Revised: 14 March 2018 / Accepted: 17 March 2018 / Published: 20 March 2018
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Abstract

In the production of cold-rolled strip, the strip surface may suffer from various defects which need to be detected and identified using an online inspection system. The system is equipped with high-speed and high-resolution cameras to acquire images from the moving strip surface. Features are then extracted from the images and are used as inputs of a pre-trained classifier to identify the type of defect. New types of defect often appear in production. At this point the pre-trained classifier needs to be quickly retrained and deployed in seconds to meet the requirement of the online identification of all defects in the environment of a continuous production line. Therefore, the method for extracting the image features and the training for the classification model should be automated and fast enough, normally within seconds. This paper presents our findings in investigating the computational and classification performance of various feature extraction methods and classification models for the strip surface defect identification. The methods include Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF) and Local Binary Patterns (LBP). The classifiers we have assessed include Back Propagation (BP) neural network, Support Vector Machine (SVM) and Extreme Learning Machine (ELM). By comparing various combinations of different feature extraction and classification methods, our experiments show that the hybrid method of LBP for feature extraction and ELM for defect classification results in less training and identification time with higher classification accuracy, which satisfied online real-time identification. View Full-Text
Keywords: machine learning; LBP; ELM; surface inspection; defect identification machine learning; LBP; ELM; surface inspection; defect identification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Liu, Y.; Xu, K.; Wang, D. Online Surface Defect Identification of Cold Rolled Strips Based on Local Binary Pattern and Extreme Learning Machine. Metals 2018, 8, 197.

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