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Sensors Technology in Cultural Heritage
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Sensors Technology in Cultural Heritage

A topical collection in Sensors (ISSN 1424-8220). This collection belongs to the section "Physical Sensors".

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Editors

Prof. Dr. Maria Koui

E-Mail Website
Collection Editor
NDT Laboratory, Department of Materials Science & Engineering, School of Chemical Engineering, National Technical University of Athens, Iroon Polytechniou No. 9, Zografou Campus, 15780 Athens, Greece
Interests: non-destructive testing; characterization of materials; protection of monuments; spectrothermography
Dr. Stefano Sfarra

E-Mail Website
Collection Editor
Department of Industrial and Information Engineering and Economics, University of L’Aquila, L'Aquila, Italy
Interests: building heritage; building pathology; infrared thermography; hygrothermal behaviour of buildings; energy efficiency; thermal comfort; numerical modelling; heat transfer; optical metrology; composite materials; NDT
Special Issues, Collections and Topics in MDPI journals
Dr. Stefano Paoloni

E-Mail Website
Collection Editor
Dipartimento di Ingegneria Industriale, Università degli Studi di Roma Tor Vergata, via del Politecnico 1, 00133 Roma, Italy
Interests: 3d imaging; cultural heritage; pulsed infrared thermography; thermal diffusivity; infrared analysis; infrared imaging
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

Cultural heritage is in danger. Historic buildings, medieval walls, castles, antique furniture, movable objects such as panel paintings, paintings on canvas, frescoes, and marqueteries are constantly subjected to deleterious attack by natural and anthropogenic agents. Severe weather events, invasive plants, no preventive maintenance, earthquakes, wars and pollution are threatening their integrity, which has stood for centuries.

Without the action of experts in the field of conservation, our heritage could crumble, their stories lost forever, and our landscape/memories could be thus changed irreversibly. The transmission of knowledge to future generations is pivotal for our society to keep our past alive and move towards the future. Among other figures, scientists can play a crucial role in the transmission of knowledge. In their work, scientists exploit the potentialities offered by portable sensor technologies, usually based on  transducers made of innovative nanomaterials, miniaturized integrated sensors, the wireless transmission of analytical signals, ICT—Information Communication Technology, IoT—Internet of Things, and innovative movable tattoo sensors devices that are fundamental for unmovable artworks. Such instruments have been proven to be valuable tools for the investigation and conservation of cultural heritage, since they are both non-invasive and non-destructive. In addition, the combination of the information obtained from different sensors is nowadays a smart approach to be pursued.

The aim of this Topical Collection is to summarize new research and developments in the field of sensors technology for the study and diagnosis of artworks and monuments. Moreover, the proposal of new procedures specifically designed for the analysis of data obtained by means of such sensors is also of great interest. We therefore invite the submission of original contributions, so that current research trends can be presented in this collection. 

Prof. Dr. Maria Koui
Prof. Dr. Stefano Sfarra
Dr. Stefano Paoloni
Collection Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the collection website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cultural heritage
  • sensors technology
  • applied physics
  • imaging
  • in situ conservation
  • analytical diagnosis

Published Papers (3 papers)

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2023

Jump to: 2022

14 pages, 38948 KiB  
Article
Generative Deep Learning-Based Thermographic Inspection of Artwork
by Yi Liu, Fumin Wang, Zhili Jiang, Stefano Sfarra, Kaixin Liu and Yuan Yao
Sensors 2023, 23(14), 6362; https://doi.org/10.3390/s23146362 - 13 Jul 2023
Viewed by 851
Abstract
Infrared thermography is a widely utilized nondestructive testing technique in the field of artwork inspection. However, raw thermograms often suffer from problems, such as limited quantity and high background noise, due to limitations inherent in the acquisition equipment and experimental environment. To overcome [...] Read more.
Infrared thermography is a widely utilized nondestructive testing technique in the field of artwork inspection. However, raw thermograms often suffer from problems, such as limited quantity and high background noise, due to limitations inherent in the acquisition equipment and experimental environment. To overcome these challenges, there is a growing interest in developing thermographic data enhancement methods. In this study, a defect inspection method for artwork based on principal component analysis is proposed, incorporating two distinct deep learning approaches for thermographic data enhancement: spectral normalized generative adversarial network (SNGAN) and convolutional autoencoder (CAE). The SNGAN strategy focuses on augmenting the thermal images, while the CAE strategy emphasizes enhancing their quality. Subsequently, principal component thermography (PCT) is employed to analyze the processed data and improve the detectability of defects. Comparing the results to using PCT alone, the integration of the SNGAN strategy led to a 1.08% enhancement in the signal-to-noise ratio, while the utilization of the CAE strategy resulted in an 8.73% improvement. Full article
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2022

Jump to: 2023

11 pages, 2334 KiB  
Article
A Spatiotemporal Deep Neural Network Useful for Defect Identification and Reconstruction of Artworks Using Infrared Thermography
by Morteza Moradi, Ramin Ghorbani, Stefano Sfarra, David M.J. Tax and Dimitrios Zarouchas
Sensors 2022, 22(23), 9361; https://doi.org/10.3390/s22239361 - 01 Dec 2022
Cited by 2 | Viewed by 1680
Abstract
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 [...] Read more.
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. Full article
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16 pages, 4670 KiB  
Review
Thermographic Imaging in Cultural Heritage: A Short Review
by Vasiliki Dritsa, Noemi Orazi, Yuan Yao, Stefano Paoloni, Maria Koui and Stefano Sfarra
Sensors 2022, 22(23), 9076; https://doi.org/10.3390/s22239076 - 23 Nov 2022
Cited by 3 | Viewed by 1745
Abstract
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 [...] Read more.
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. Full article
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