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Terahertz Localization and Classification for Damages in GFRP Composites Using Complex Network with Residual Attention Mechanism
编号：47 访问权限：仅限参会人 更新：2025-11-10 11:23:37 浏览：13次 口头报告

报告开始：2025年11月22日 16:20（Asia/Shanghai）

报告时间：20min

所在会场：[S4] Parallel Session 4 [S4-1] Parallel Session 4-22 PM

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摘要
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.
 
关键词
GFRP composites, Terahertz imaging, Multi-head attention mechanism, Residual network, Nondestructive testing
报告人
Mn Zhai
Assistant Professor Shenzhen University

稿件作者
Mn Zhai Shenzhen University
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重要日期

  • 会议日期

    11月21日

    2025

    至

    11月23日

    2025

  • 10月20日 2025

    初稿截稿日期

  • 11月23日 2025

    注册截止日期

主办单位
IEEE Instrumentation and Measurement Society
South China University of Technology
承办单位
South China University of Technology
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