Infrared machine vision and infrared thermography with deep learning: A review

Y He, B Deng, H Wang, L Cheng, K Zhou, S Cai… - Infrared physics & …, 2021 - Elsevier
Infrared imaging-based machine vision (IRMV) is the technology used to automatically
inspect, detect, and analyse infrared images (or videos) obtained by recording the intensity …

Non-destructive techniques (NDT) for the diagnosis of heritage buildings: Traditional procedures and futures perspectives

B Tejedor, E Lucchi, D Bienvenido-Huertas, I Nardi - Energy and Buildings, 2022 - Elsevier
It is estimated that EU cultural heritage (CH) buildings represent 30% of the total existing
stock. Nevertheless, all actions in terms of refurbishment need a deep knowledge based on …

[HTML][HTML] Advancements in fiber-reinforced polymer composite materials damage detection methods: Towards achieving energy-efficient SHM systems

O Ahmed, X Wang, MV Tran, MZ Ismadi - Composites Part B: Engineering, 2021 - Elsevier
The application of fiber-reinforced polymer (FRP) composites is continuously increasing due
to their superior mechanical properties and the associated weight advantage. However, they …

Intelligent structural health monitoring of composite structures using machine learning, deep learning, and transfer learning: a review

MM Azad, S Kim, YB Cheon, HS Kim - Advanced Composite …, 2024 - Taylor & Francis
Structural health monitoring (SHM) methods are essential to guarantee the safety and
integrity of composite structures, which are extensively utilized in aerospace, automobile …

Noncontact sensing techniques for AI-aided structural health monitoring: a systematic review

A Sabato, S Dabetwar, NN Kulkarni… - IEEE Sensors …, 2023 - ieeexplore.ieee.org
Engineering structures and infrastructure continue to be used despite approaching or having
reached their design lifetime. While contact-based measurement techniques are challenging …

Defect identification in composite materials via thermography and deep learning techniques

HT Bang, S Park, H Jeon - Composite Structures, 2020 - Elsevier
Composite materials are widely used in aircraft, vehicle, and various industries due to their
excellent mechanical properties. A thermography-based nondestructive test is often …

Defect detection in composites by deep learning using solitary waves

S Yoon, WJ Cantwell, CY Yeun, CS Cho… - International Journal of …, 2023 - Elsevier
This paper proposes a real-time non-destructive evaluation technique to detect defects in
laminated composites by deep learning using highly nonlinear solitary waves (HNSWs) …

Machine learning for ultrasonic nondestructive examination of welding defects: A systematic review

H Sun, P Ramuhalli, RE Jacob - Ultrasonics, 2023 - Elsevier
Recent years have seen a substantial increase in the application of machine learning (ML)
for automated analysis of nondestructive examination (NDE) data. One of the applications of …

Depth-wise Squeeze and Excitation Block-based Efficient-Unet model for surface defect detection

H Üzen, M Turkoglu, M Aslan, D Hanbay - The Visual Computer, 2023 - Springer
Detection of surface defects in manufacturing systems is crucial for product quality. Detection
of surface defects with high accuracy can prevent financial and time losses. Recently, efforts …

Development of a physics-informed doubly fed cross-residual deep neural network for high-precision magnetic flux leakage defect size estimation

H Sun, L Peng, S Huang, S Li, Y Long… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
Defect depth is an essential indicator in magnetic flux leakage (MFL) detection and
estimation. The quantification errors for defect depth are closely related to length and width …