TY - GEN
T1 - RCTNet-A Defect Detection Network for Tiny Debonding Defects in Terahertz Images
AU - Wang, Rong
AU - Xu, Yafei
AU - Wang, Xingyu
AU - Zhang, Liuyang
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Due to the limitation of terahertz wavelength and serious development of terahertz devices, terahertz imaging for non-destructive testing has been severely constrained by insufficient data, low spatial resolution, blurred contours and high background noise. In this paper, a THz- TDS system is utilized to conduct experiments on defective samples of composite materials, and time-slice imaging is utilized to obtain defective terahertz image datasets. A THz-RCTNet is proposed to identify the defects with high accuracy, in which CSSPP module and modified loss function effectively increase the detection accuracy of the defects. The model can accurately identify tiny defects and achieve the high-precision localization of defects. In summary, our proposed method is conducive to further development of terahertz image based nondestructive testing technology and fullfil the requirements of realtime defect detection in industrial applications at an early stage.
AB - Due to the limitation of terahertz wavelength and serious development of terahertz devices, terahertz imaging for non-destructive testing has been severely constrained by insufficient data, low spatial resolution, blurred contours and high background noise. In this paper, a THz- TDS system is utilized to conduct experiments on defective samples of composite materials, and time-slice imaging is utilized to obtain defective terahertz image datasets. A THz-RCTNet is proposed to identify the defects with high accuracy, in which CSSPP module and modified loss function effectively increase the detection accuracy of the defects. The model can accurately identify tiny defects and achieve the high-precision localization of defects. In summary, our proposed method is conducive to further development of terahertz image based nondestructive testing technology and fullfil the requirements of realtime defect detection in industrial applications at an early stage.
KW - Defect detection
KW - Non-destructive testing (NDT)
KW - THz image
KW - Transformer
UR - https://www.scopus.com/pages/publications/85191496606
U2 - 10.1109/ICSMD60522.2023.10490863
DO - 10.1109/ICSMD60522.2023.10490863
M3 - 会议稿件
AN - SCOPUS:85191496606
T3 - ICSMD 2023 - International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, Proceedings
BT - ICSMD 2023 - International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2023
Y2 - 2 November 2023 through 4 November 2023
ER -