Abstract
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.
Highlights
In the industrial production process, due to the deficiencies and limitations of existing technology, working conditions, and other factors, the quality of manufactured products is extremely affected
Literature [78] developed a WSL framework composed of localization network (LNet) and decision network (DNet) for steel surface defect detection, where LNet uses image-level label training and outputs the heat map of potential defect locations as DNet input, DNet uses RSAM to weight the regions identified by LNet, the performance of the proposed framework is proven on real industrial data sets
Among the three methods of deep learning, the supervised method is the most widely used because of its good accuracy, but it has obvious disadvantages; the unsupervised method is in line with the process of industrial development but has its own characteristics; the weakly supervised method is not widely used at present, but it has a broad development prospect
Summary
In the industrial production process, due to the deficiencies and limitations of existing technology, working conditions, and other factors, the quality of manufactured products is extremely affected. In order to solve the above problems, this paper firstly summarizes the research status of industrial product surface defect detection from the traditional machine vision method and deep learning method, after that, the key problems in the process of industrial surface defect detection, real-time problems, small sample problems, small target problem, unbalanced sample problem, are discussed, and some solutions for each problem are given. The main contents of this paper are as follows: Section 2, the summary of industrial product surface defect detection methods based on traditional feature-based machine vision algorithm; Section 3, the summary of industrial product surface defect detection methods based on deep learning; Section 4, key problems and their solutions analysis and discussion; Section 5, the collation and summary of the industrial product surface defect detection dataset and the comparison of the latest methods of the MVTec. 2.
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