A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data
It addresses the problem of costly data annotation in industrial quality inspection for manufacturers, but it is a survey paper, so it is incremental by summarizing existing work.
This paper reviews surface defect detection methods for industrial products that require only a small number of labeled data, categorizing them into traditional image processing and deep learning approaches.
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.