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

arXiv: https://arxiv.org/abs/2203.05733 · HTML: https://mlsanity.com/paper/2203.05733/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 89 references that are in the map.
- First posted on arXiv 11 Mar 2022.

## Builds on (most cited first, 2 of 2)

- [Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning](https://mlsanity.com/paper/1808.02518/) (2018)
- [Gabor wavelets combined with volumetric fractal dimension applied to texture analysis](https://mlsanity.com/paper/1412.7856/) (2014)

## Built on by (most cited first, 2 of 2)

- [A Comprehensive Survey on Machine Learning Driven Material Defect Detection](https://mlsanity.com/paper/2406.07880/) (2024)
- [SynSur: An end-to-end generative pipeline for synthetic industrial surface defect generation and detection](https://mlsanity.com/paper/2604.26633/) (2026)

## Abstract (arXiv)

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.