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Terahertz Localization and Classification for Damages in GFRP Composites Using Complex Network with Residual Attention Mechanism
ID：47 View Protection：ATTENDEE Updated Time：2025-11-10 11:23:37 Hits：156 Oral Presentation

Start Time：2025-11-22 16:20（Asia/Shanghai）

Duration：20min

Session：S4 Parallel Session 4 » S4-1Parallel Session 4-22 PM

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Abstract
Terahertz (THz) reflectometry has been employed to quality control of Glass fiber-reinforced polymer (GFRP) composites in a nondestructive and contactless fashion. Owing to relatively long wavelength of THz waves, as well as diffraction and absorption dispersion during propagation, THz imaging fails to visualize subtle damage in GFRP composites. We design one lightweight deep-learning network based on multiscale residual attention mechanism to characterize damage at different depths in GFRP composites. The experimental results demonstrate that the proposed framework can not only enhance the spatial resolution of THz images but also achieve high-precision localization and classification of hidden damages. Compared to classic object detection models, our method has proved to be beneficial to improve the accuracy of damages characterization within THz-nondestructive testing (NDT) scenarios.
 
Keywords
GFRP composites, Terahertz imaging, Multi-head attention mechanism, Residual network, Nondestructive testing
Speaker
Mn Zhai
Assistant Professor Shenzhen University

Submission Author
Mn Zhai Shenzhen University
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Important Date

  • Conference Date

    Nov 21

    2025

    to

    Nov 23

    2025

  • Oct 20 2025

    Draft paper submission deadline

  • Dec 08 2025

    Registration deadline

Sponsored By
IEEE Instrumentation and Measurement Society
South China University of Technology
Organized By
South China University of Technology
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