  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.
