Unsupervised Anomaly Detection in Printed Circuit Boards through Student–Teacher Feature Pyramid Matching

Unsupervised Anomaly Detection in Printed Circuit Boards through Student–Teacher Feature Pyramid Matching

Deep learning methods are currently used in industries to improve the efficiency and quality of the product. Detecting defects on printed circuit boards (PCBs) is a challenging task and is usually solved by automated visual inspection, automated optical inspection, manual inspection, and supervised...

Full description

Bibliographic Details
Main Authors: Venkat Anil Adibhatla, Yu-Chieh Huang, Ming-Chung Chang, Hsu-Chi Kuo, Abhijeet Utekar, Huan-Chuang Chih, Maysam F. Abbod, Jiann-Shing Shieh
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Electronics
Subjects:
deep learning
printed circuit board
anomaly detection
unsupervised learning
student–teacher feature pyramid matching
Online Access:https://www.mdpi.com/2079-9292/10/24/3177

Internet

https://www.mdpi.com/2079-9292/10/24/3177

Similar Items