Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

Qiwu Luo, Yichuang Sun, Pengcheng Li, Oluyomi Simpson, Lu Tian, Yigang He

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)
212 Downloads (Pure)

Abstract

Efficient defect classification is one of the most important preconditions to achieve online quality inspection for hot-rolled strip steels. It is extremely challenging owing to various defect appearances, large intraclass variation, ambiguous interclass distance, and unstable gray values. In this paper, a generalized completed local binary patterns (GCLBP) framework is proposed. Two variants of improved completed local binary patterns (ICLBP) and improved completed noise-invariant local-structure patterns (ICNLP) under the GCLBP framework are developed for steel surface defect classification. Different from conventional local binary patterns variants, descriptive information hidden in nonuniform patterns is innovatively excavated for the better defect representation. This paper focuses on the following aspects. First, a lightweight searching algorithm is established for exploiting the dominant nonuniform patterns (DNUPs). Second, a hybrid pattern code mapping mechanism is proposed to encode all the uniform patterns and DNUPs. Third, feature extraction is carried out under the GCLBP framework. Finally, histogram matching is efficiently accomplished by simple nearest-neighbor classifier. The classification accuracy and time efficiency are verified on a widely recognized texture database (Outex) and a real-world steel surface defect database [Northeastern University (NEU)]. The experimental results promise that the proposed method can be widely applied in online automatic optical inspection instruments for hot-rolled strip steel.

Original languageEnglish
Pages (from-to)667-679
Number of pages13
JournalIEEE Transactions on Instrumentation and Measurement
Volume68
Issue number3
Early online date27 Jul 2018
DOIs
Publication statusPublished - 1 Mar 2019

Keywords

  • Automatic optical inspection (AOI) instrument
  • Databases
  • Feature extraction
  • Histograms
  • hot-rolled strips
  • image classification
  • Inspection
  • Instruments
  • local binary patterns (LBP)
  • Steel
  • Strips
  • surface defects.
  • surface defects

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