A Survey of Semi-supervised and Unsupervised Learning Methods for Industrial Defect Anomaly Detection
DOI:
https://doi.org/10.63313/JCSFT.9001Keywords:
Industrial defect detection, Unsupervised learning, Semi-supervised learning, Deep learning, Anomaly detection, Quality controlAbstract
With the continuous advancement of industrial automation, intelligent defect detection has become a crucial means of ensuring product quality. However, the cost and labor required to obtain high-quality labeled data limit the widespread practical application of traditional supervised learn-ing methods. Therefore, semi-supervised learning (SSL) and unsupervised learning (UL) methods have received extensive attention from researchers due to their superior performance in low-labeled or unlabeled scenarios. This paper provides a systematic survey of typical semi-supervised and unsupervised learning methods in the field of industrial defect detection, analyzes the core concepts and key technologies of unsupervised learning and semi-supervised learning, as well as compara-tive analysis on relevant datasets, and finally proposes future development directions.
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