Surface Defect Detection Methods for Industrial Products: A Review

The comprehensive intelligent development of the manufacturing industry puts forward new requirements for the quality inspection of industrial products. This paper summarizes the current research status of machine learning methods in surface defect detection, a key part in the quality inspection of...

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Bibliographic Details
Published inApplied sciences Vol. 11; no. 16; p. 7657
Main Authors Chen, Yajun, Ding, Yuanyuan, Zhao, Fan, Zhang, Erhu, Wu, Zhangnan, Shao, Linhao
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 20.08.2021
Subjects
Color
Datasets
Deep learning
defect detection
Defects
image dataset
Image retrieval
Industrial products
Inspection
Learning algorithms
Machine learning
Machine vision
Manufacturing industry
R&D
Random variables
Research & development
Research methods
Signal processing
Statistical analysis
Statistical methods
Surface defects
Technology
unbalanced samples
Vision systems
Wavelet transforms
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Summary:The comprehensive intelligent development of the manufacturing industry puts forward new requirements for the quality inspection of industrial products. This paper summarizes the current research status of machine learning methods in surface defect detection, a key part in the quality inspection of industrial products. First, according to the use of surface features, the application of traditional machine vision surface defect detection methods in industrial product surface defect detection is summarized from three aspects: texture features, color features, and shape features. Secondly, the research status of industrial product surface defect detection based on deep learning technology in recent years is discussed from three aspects: supervised method, unsupervised method, and weak supervised method. Then, the common key problems and their solutions in industrial surface defect detection are systematically summarized; the key problems include real-time problem, small sample problem, small target problem, unbalanced sample problem. Lastly, the commonly used datasets of industrial surface defects in recent years are more comprehensively summarized, and the latest research methods on the MVTec AD dataset are compared, so as to provide some reference for the further research and development of industrial surface defect detection technology.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11167657