A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data

Qifan Jin; Li Chen

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arXiv · 2203.05733

A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data

Qifan Jin·Li Chen

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Abstract

The surface defect detection method based on visual perception has been widely used in industrial quality inspection. Because defect data are not easy to obtain and the annotation of a large number of defect data will waste a lot of manpower and material resources. Therefore, this paper reviews the methods of surface defect detection of industrial products based on a small number of labeled data, and this method is divided into traditional image processing-based industrial product surface defect detection methods and deep learning-based industrial product surface defect detection methods suitable for a small number of labeled data. The traditional image processing-based industrial product surface defect detection methods are divided into statistical methods, spectral methods and model methods. Deep learning-based industrial product surface defect detection methods suitable for a small number of labeled data are divided into based on data augmentation, based on transfer learning, model-based fine-tuning, semi-supervised, weak supervised and unsupervised.

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Qifan Jin, Li Chen. 2022-03-11. A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data. https://arxiv.org/abs/2203.05733

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