Automatic terahertz recognition of hidden defects in layered polymer composites based on a deep residual network with transfer learning

Liu, Wenquan; Wang, Qiang; Zhang, Hanlong; Li, Zhenyuan; Liu, Qiuhan; She, Rongbin; Zhang, Rui

doi:10.1109/irmmw-thz50926.2021.9567008

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…loss, frequency and electrical distributions for THz QCL lasers with distributed feedback [116]. The characterization of kernel size of the convolutional layers for THz deep learning models for high precision THz tomography was presented by Hung and Yang [117] and Transfer learning was demonstrated for automatic recognition of defects hidden in fiber reinforced polymer based THz nondestructive technology [118]. The security inspection based on deep learning and THz imaging technology is another application that has been leveraged to detect dangerous goods and hidden dangerous objects with accuracy and speed that meets the optimum security check requirements [119]- [123] as well as in industrial inspection THz applications for recognition of defects in integrated circuits (IC) [124], plastics and ceramics in real-time manufacturing process [125] [126] and nondestructive testing of impurities in wheat grains [127].…”

Section: B Classification Detection and Identificationmentioning

confidence: 99%

Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications

Gezimati,

Singh

2024

IEEE Access

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Following the recent progress in the development of Terahertz (THz) generation and detection, THz technology is being widely used to characterize test sample properties in various applications including nondestructive testing, security inspection and medical applications. In this paper, we have presented a broad review of the recent usage of artificial intelligence (AI) particularly, deep learning techniques in various THz sensing, imaging, and spectroscopic applications with emphasis on their implementation for medical imaging of cancerous cells. Initially, the fundamentals principles and techniques for THz generation and detection, imaging and spectroscopy are introduced. Subsequently, a brief overview of AI -machine learning and deep learning techniques is summarized, and their performance is compared. Further, the usage of deep learning algorithms in various THz applications is reported, with focus on metamaterials design and classification, detection, reconstruction, segmentation, parameter extraction and denoising tasks. Moreover, we also report the metrics used to evaluate the performance of deep learning models and finally, the existing research challenges in the application of deep learning in THz cancer imaging applications are identified and possible solutions are suggested through emerging trends. With the continuous increase of acquired THz data -sensing, spectral and imaging, artificial intelligence has emerged as a dominant paradigm for embedded data extraction, understanding, perception, decision making and analysis. Towards this end, the integration of state-of-the-art machine learning techniques such as deep learning with THz applications enable detailed computational and theoretical analysis for better validation and verification than modelling techniques that precede the era of machine learning. The study will facilitate the large-scale clinical applications of deep learning enabled THz imaging systems for the development of smart and connected next generation healthcare systems as well as provide a roadmap for future research direction.

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Section: B Classification Detection and Identificationmentioning

confidence: 99%

Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications

Gezimati,

Singh

2024

IEEE Access

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“…Wang et al [22] and Xu et al [23] used a CNN to automatically detect and classify the internal bonding defects of glass fiber-reinforced plastics, actualizing automatic defect recognition and location. Liu et al [24] realized the automatic recognition of different defects by constructing a deep residual network to recognize bonding defects in fiber-reinforced plastics. Ren et al [25] and [26] achieved a recognition accuracy of more than 90% for a specific dataset using a back-propagation (BP) neural network to recognize the artificial pre-embedded defects of the adhesive bonding structure.…”

Section: Introductionmentioning

confidence: 99%

Defect identification in adhesive structures using multi-Feature fusion convolutional neural network

et al. 2023

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The interface-debonding defects of adhesive bonding structures may cause a reduction in bonding strength, which in turn affects the bonding quality of adhesive bonding samples. Hence, defect recognition in adhesive bonding structures is particularly important. In this study, a terahertz (THz) wave was used to analyze bonded structure samples, and a multi-feature fusion convolutional neural network (CNN) was used to identify the defect waveforms. The pooling method of the squeeze-and-excitation (SE) attention mechanism was optimized, defect feature weights were adaptively assigned, and feature fusion was conducted using automatic label net-works to segment the THz waveforms in the adhesive bonding area with fine granularity waveforms as an input to the multi-channel CNN. The results revealed that the speed of the THz waveform labeling with the automatic labeling network was 10 times higher than that with traditional methods, and the defect-recognition accuracy of the defect-recognition network constructed in this study was up to 99.28%. The F1-score was 99.73%, and the lowest pre-embedded defect recognition error rate of the generalization experiment samples was 0.27%.

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DA-CNN-based similar terahertz signal identification for intelligent characterization of internal debonding defects of composites under high-resolution mode

Wang

Cui

et al. 2023

Composite Structures

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Automatic terahertz recognition of hidden defects in layered polymer composites based on a deep residual network with transfer learning

Cited by 3 publications

References 3 publications

Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications

Terahertz Data Extraction and Analysis Based on Deep Learning Techniques for Emerging Applications

Defect identification in adhesive structures using multi-Feature fusion convolutional neural network

DA-CNN-based similar terahertz signal identification for intelligent characterization of internal debonding defects of composites under high-resolution mode

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