University of Oulu

Q. Luo et al., "Surface Defect Classification for Hot-Rolled Steel Strips by Selectively Dominant Local Binary Patterns," in IEEE Access, vol. 7, pp. 23488-23499, 2019. doi: 10.1109/ACCESS.2019.2898215

Surface defect classification for hot-rolled steel strips by selectively dominant local binary patterns

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Author: Luo, Qiwu1; Fang, Xiaoxin2; Sun, Yichuang3;
Organizations: 1School of Automation, Central South University, Changsha, China
2School of Electrical and Automation Engineering, Hefei University of Technology, Hefei, China
3School of Engineering and Technology, University of Hertfordshire, Hatfield, U.K.
4Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
5School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 9.4 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2019-10-10


Developments in defect descriptors and computer vision-based algorithms for automatic optical inspection (AOI) allows for further development in image-based measurements. Defect classification is a vital part of an optical-imaging-based surface quality measuring instrument. The high-speed production rhythm of hot continuous rolling requires an ultra-rapid response to every component as well as algorithms in AOI instrument. In this paper, a simple, fast, yet robust texture descriptor, namely selectively dominant local binary patterns (SDLBPs), is proposed for defect classification. First, an intelligent searching algorithm with a quantitative thresholding mechanism is built to excavate the dominant non-uniform patterns (DNUPs). Second, two convertible schemes of pattern code mapping are developed for binary encoding of all uniform patterns and DNUPs. Third, feature extraction is carried out under SDLBP framework. Finally, an adaptive region weighting method is built for further strengthening the original nearest neighbor classifier in the feature matching stage. The extensive experiments carried out on an open texture database (Outex) and an actual surface defect database (Dragon) indicates that our proposed SDLBP yields promising performance on both classification accuracy and time efficiency.

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Series: IEEE access
ISSN: 2169-3536
ISSN-E: 2169-3536
ISSN-L: 2169-3536
Volume: 7
Pages: 23488 - 23499
Article number: 8638771
DOI: 10.1109/ACCESS.2019.2898215
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This work was supported in part by the National Natural Science Foundation of China under Grant 51704089 and Grant 61701157, in part by the Anhui Provincial Natural Science Foundation of China under Grant 1808085QF190 and Grant 1808085QF206, in part by the China Postdoctoral Science Foundation under Grant 2017M621996, and in part by the Fundamental Research Funds for the Central Universities of China under Grant JZ2018YYPY0296.
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