A Survey of Surface Defect Detection Based on Deep Learning
Authors
*Corresponding author.
Email: xiaohua007qaq@outlook.com
Corresponding Author
Weihua Yang
Available Online 9 December 2022.
- DOI
- 10.2991/978-2-494069-51-0_51How to use a DOI?
- Keywords
- Machine vision; Surface defect detection; Deep learning; Convolutional neural network (CNN)
- Abstract
In recent years, with the rapid development of technologies such as computers and artificial intelligence, various research fields based on deep learning have been broadly used, among which industrial detection is the most important. In this paper, the definition of defects and defect detection is firstly defined. Then, several mainstream methods of surface defect detection based on convolutional neural network are introduced in recent years, and the typical application scenarios of each method are summarized. Finally, two key problems in surface defect detection are discussed.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Weihua Yang PY - 2022 DA - 2022/12/09 TI - A Survey of Surface Defect Detection Based on Deep Learning BT - Proceedings of the 2022 7th International Conference on Modern Management and Education Technology (MMET 2022) PB - Atlantis Press SP - 362 EP - 367 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-494069-51-0_51 DO - 10.2991/978-2-494069-51-0_51 ID - Yang2022 ER -