IRT for NDT: 2050 Horizon

Khodayar, Fariba; Sojasi, Saeed; Maldague, Xavier

doi:10.21611/qirt.2015.0001

Cited by 4 publications

(1 citation statement)

References 48 publications

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“…Although the first IR cameras on the market date from 1965 [ 20 ], IRT can still be considered an up-to-date inspection technology due to the continuous evolution of this technology, including within the cultural heritage field. This evolution concerns both IRT data acquisition methods, such as the continuous improvement of the IR cameras and the new forms of thermal excitation (such as induced eddy current, and microwave and ultrasound excitation), and IRT data processing algorithms.…”

Section: Introductionmentioning

confidence: 99%

Introduction of Deep Learning in Thermographic Monitoring of Cultural Heritage and Improvement by Automatic Thermogram Pre-Processing Algorithms

Garrido

Erazo-Aux

Lagüela

et al. 2021

Sensors

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The monitoring of heritage objects is necessary due to their continuous deterioration over time. Therefore, the joint use of the most up-to-date inspection techniques with the most innovative data processing algorithms plays an important role to apply the required prevention and conservation tasks in each case study. InfraRed Thermography (IRT) is one of the most used Non-Destructive Testing (NDT) techniques in the cultural heritage field due to its advantages in the analysis of delicate objects (i.e., undisturbed, non-contact and fast inspection of large surfaces) and its continuous evolution in both the acquisition and the processing of the data acquired. Despite the good qualitative and quantitative results obtained so far, the lack of automation in the IRT data interpretation predominates, with few automatic analyses that are limited to specific conditions and the technology of the thermographic camera. Deep Learning (DL) is a data processor with a versatile solution for highly automated analysis. Then, this paper introduces the latest state-of-the-art DL model for instance segmentation, Mask Region-Convolution Neural Network (Mask R-CNN), for the automatic detection and segmentation of the position and area of different surface and subsurface defects, respectively, in two different artistic objects belonging to the same family: Marquetry. For that, active IRT experiments are applied to each marquetry. The thermal image sequences acquired are used as input dataset in the Mask R-CNN learning process. Previously, two automatic thermal image pre-processing algorithms based on thermal fundamentals are applied to the acquired data in order to improve the contrast between defective and sound areas. Good detection and segmentation results are obtained regarding state-of-the-art IRT data processing algorithms, which experience difficulty in identifying the deepest defects in the tests. In addition, the performance of the Mask R-CNN is improved by the prior application of the proposed pre-processing algorithms.

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Section: Introductionmentioning

confidence: 99%

Introduction of Deep Learning in Thermographic Monitoring of Cultural Heritage and Improvement by Automatic Thermogram Pre-Processing Algorithms

Garrido

Erazo-Aux

Lagüela

et al. 2021

Sensors

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Low-Velocity Impact Damage Quantification on Sandwich Panels by Thermographic and Ultrasonic Procedures

et al. 2023

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Composite sandwich structures are widely used for their mechanical properties combined to lightweight. However, damage area quantification caused by low velocity impacts represents generally a crucial task in sandwich composites. In the last years, recent advantages of thermographic devices offer new promising and different real-time industrial and engineering applications where lower computation time, accuracy of results and convenient cost are required. The present research deals with the comparison of standard or latest image-processing methods proposed for pulsed thermography regarding their suitability for determining the impact damage area in sandwich materials made of Aluminium core an a GFRP laminated skins. The Infra-Red processed results are compared with the advanced ultrasonic Phased array method commonly employed in the industrial Non-Destructive Testing. Specifically, the damage area quantification is performed by means of an appropriate MATLAB binarization algorithm for the post-processing of acquired thermal and ultrasonic maps. The data results verify the effectiveness of the image-processing thermographic technique combined to advanced processing approaches for the quantitative assessment of impact damage in sandwich component.

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Thermographic methodologies used in infrastructure inspection: A review—data acquisition procedures

Garrido

Lagüela

Otero

et al. 2020

Infrared Physics & Technology

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IRT for NDT: 2050 Horizon

Cited by 4 publications

References 48 publications

Introduction of Deep Learning in Thermographic Monitoring of Cultural Heritage and Improvement by Automatic Thermogram Pre-Processing Algorithms

Introduction of Deep Learning in Thermographic Monitoring of Cultural Heritage and Improvement by Automatic Thermogram Pre-Processing Algorithms

Low-Velocity Impact Damage Quantification on Sandwich Panels by Thermographic and Ultrasonic Procedures

Thermographic methodologies used in infrastructure inspection: A review—data acquisition procedures

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