期刊
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
卷 68, 期 3, 页码 667-679出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2018.2852918
关键词
Automatic optical inspection (AOI) instrument; hot-rolled strips; image classification; local binary patterns (LBP); surface defects
资金
- National Natural Science Foundation of China [51704089, 51577046, 51637004]
- Anhui Provincial Natural Science Foundation of China [1808085QF190]
- China Postdoctoral Science Foundation [2017M621996]
- Fundamental Research Funds for the Central Universities of China [JZ2018YYPY0296]
- National Key Research and Development Plan Important Scientific Instruments and Equipment Development [2016YFF0102200]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据