摘要详情

ID / 提交时间

27 / 2023-08-28 16:56:48

标题

RCTNet-A Defect Detection Network for Tiny Debonding Defects in Terahertz Images

关键字

THZ-image,Transformer,Defect detection,Non-Destructive Testing

主题及专题

Special Sessions > SS05 - Computer Vision for Automation Science and Engineering

状态

终稿

作者

Rong Wang / State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an

Yafei Xu / State Key Laboratory for Manufacturing Systems Engineering； Xi’an Jiaotong University

Xingyu Wang / State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University, Xi’an

留洋张 / State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an

摘要

Due to the limitation of terahertz wavelength and serious lag in the 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, 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 meet the requirements of real-time defect detection in industrial applications at an early stage.