Automatic terahertz recognition of hidden defects in layered polymer composites based on a deep residual network with transfer learning
- Resource Type
- Conference
- Authors
- Liu, Wenquan; Wang, Qiang; Zhang, Hanlong; Li, Zhenyuan; Liu, Qiuhan; She, Rongbin; Zhang, Rui
- Source
- 2021 46th International Conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz) Infrared, Millimeter and Terahertz Waves (IRMMW-THz),2022 47th International Conference on. :1-1 Aug, 2021
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Photonics and Electrooptics
Signal Processing and Analysis
Transfer learning
Training data
Sensitivity and specificity
Optical fiber networks
Polymers
Residual neural networks
- Language
- ISSN
- 2162-2035
We demonstrate a deep residual network with transfer learning strategy for automatic terahertz (THz) recognition of the hidden defects in fiber reinforced polymer (FRP) composites with small-scale training data. The recognition performance with high accuracy, precision, sensitivity and specificity indicate the effectiveness of the proposed method for automatically identifying different defects in THz nondestructive applications.