Introduction of the combination of thermal fundamentals and Deep Learning for the automatic thermographic inspection of thermal bridges and water-related problems in infrastructures

Garrido, Iván; Lagüela, S.; Fang, Qiang; Arias, Pedro

doi:10.1080/17686733.2022.2060545

Cited by 17 publications

(13 citation statements)

References 47 publications

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“…Similarly, most of these methods also employ semantic segmentation networks in image processing. Garrido et al 102 proposed a defect segmentation method based on deep learning and thermal images. An automatic thermal image processing algorithm was used to enhance the thermal contrast between the defective and non-defective parts.…”

Section: Artificial Intelligence Solutions For Bridge Damage Detectionmentioning

confidence: 99%

Review of artificial intelligence-based bridge damage detection

Zhang

Yuen

2022

Advances in Mechanical Engineering

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Bridges are often located in harsh environments and are thus extremely susceptible to damage. If the initial damage is not detected in time, it can develop further causing safety hazards. Therefore, accurate detection of bridge damage is an important topic. In recent years, artificial intelligence technology has been developed rapidly, especially machine learning algorithms, which have shown amazing results in various fields while it also received attention in bridge inspection. This paper summarizes the progress of bridge damage detection research related to artificial intelligence techniques between 2015 and 2021. For structural health monitoring, sensing data is the basis for various data processing methods. The strength and weakness of the sensing data itself directly affect the effectiveness of subsequent processing methods. As a result, this paper classifies bridge damage detection studies into six categories from the types of sensing data: visual image, point cloud, infrared thermal imaging, ground-penetrating radar, vibration response, and other types of data. These six types of damage detection methods were reviewed and summarized respectively. Finally, challenges and future trends were discussed.

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Section: Artificial Intelligence Solutions For Bridge Damage Detectionmentioning

confidence: 99%

Review of artificial intelligence-based bridge damage detection

Zhang

Yuen

2022

Advances in Mechanical Engineering

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“…It should be noted that the homogeneous and less reflective colors that cover the surface of the CFRP specimens make damage detection for them easier than panel painting for IRT. For various infrastructures (including buildings, historical landmarks, and civil infrastructures), thermograms obtained were processed using the mask region-convolution neural network (Mask R-CNN) in conjunction with an automatic thermogram preprocessing algorithm with a focus on water-related issues and thermal bridges [ 28 ]. For the purpose of diagnosing breast cancer, a deep convolutional neural network (CNN) including transfer learning has been developed to automatically classify thermograms into two categories: normal and abnormal [ 29 ].…”

Section: Background On Irt In Cultural Heritage and Irt Using Aimentioning

confidence: 99%

A Spatiotemporal Deep Neural Network Useful for Defect Identification and Reconstruction of Artworks Using Infrared Thermography

Moradi

Ghorbani

Сфарра

et al. 2022

Sensors

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Assessment of cultural heritage assets is now extremely important all around the world. Non-destructive inspection is essential for preserving the integrity of artworks while avoiding the loss of any precious materials that make them up. The use of Infrared Thermography is an interesting concept since surface and subsurface faults can be discovered by utilizing the 3D diffusion inside the object caused by external heat. The primary goal of this research is to detect defects in artworks, which is one of the most important tasks in the restoration of mural paintings. To this end, machine learning and deep learning techniques are effective tools that should be employed properly in accordance with the experiment’s nature and the collected data. Considering both the temporal and spatial perspectives of step-heating thermography, a spatiotemporal deep neural network is developed for defect identification in a mock-up reproducing an artwork. The results are then compared with those of other conventional algorithms, demonstrating that the proposed approach outperforms the others.

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“…PT is one of the active infrared techniques, which uses an optical device as an external heat source. Among the active thermography techniques described above, it is the easiest to apply and widely used [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ]. From the physics point of view, PT relies on the spatially resolved detection of the transient IR emission from the sample surface (in both the mid-wave (MW) and the long-wave (LW) IR spectral ranges), typically induced by the absorption of short light pulses.…”

Section: Introductionmentioning

confidence: 99%

Thermographic Imaging in Cultural Heritage: A Short Review

Dritsa

Orazi

Yao

et al. 2022

Sensors

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Over the recent period, there has been an increasing interest in the use of pulsed infrared thermography (PT) for the non-destructive evaluation of Cultural Heritage (CH). Unlike other techniques that are commonly employed in the same field, PT enables the depth-resolved detection of different kinds of subsurface features, thus providing helpful information for both scholars and restorers. Due to this reason, several research activities are currently underway to further improve the PT effectiveness. In this manuscript, the specific use of PT for the analysis of three different types of CH, namely documentary materials, panel paintings–marquetery, and mosaics, will be reviewed. In the latter case, i.e., mosaics, passive thermography combined with ground penetrating radar (GPR) and digital microscopy (DM) have also been deepened, considering their suitability in the open field. Such items have been selected because they are characterized by quite distinct physical and structural properties and, therefore, different PT (and, in some cases, verification) approaches have been employed for their investigations.

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Introduction of the combination of thermal fundamentals and Deep Learning for the automatic thermographic inspection of thermal bridges and water-related problems in infrastructures

Cited by 17 publications

References 47 publications

Review of artificial intelligence-based bridge damage detection

Review of artificial intelligence-based bridge damage detection

A Spatiotemporal Deep Neural Network Useful for Defect Identification and Reconstruction of Artworks Using Infrared Thermography

Thermographic Imaging in Cultural Heritage: A Short Review

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