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

Autor: Elena Pivarčiová, Susana Lagüela, Clemente Ibarra-Castanedo, Gianfranco Gargiulo, Ivan Lapuente Garrido, Xavier Maldague, Pedro Arias, Jorge Erazo-Aux, Stefano Sfarra
Jazyk: angličtina
Předmět:
Infrared
Computer science
preservation
1206.01 Construcción de Algoritmos
02 engineering and technology
lcsh:Chemical technology
Thermographic camera
Biochemistry
Field (computer science)
Article
Analytical Chemistry
law.invention
Data processing system
law
Nondestructive testing
0202 electrical engineering
electronic engineering
information engineering
lcsh:TP1-1185
Segmentation
automation
cultural heritage
deep learning
infrared thermography
marquetry
mask R-CNN
monitoring
thermal principles
Electrical and Electronic Engineering
Instrumentation
business.industry
Deep learning
021001 nanoscience & nanotechnology
Automation
Atomic and Molecular Physics
and Optics
3311.02 Ingeniería de Control
Thermography
020201 artificial intelligence & image processing
Artificial intelligence
0210 nano-technology
business
Algorithm
Zdroj: Investigo. Repositorio Institucional de la Universidade de Vigo
Universidade de Vigo (UVigo)
Sensors (Basel, Switzerland)
Sensors
Volume 21
Issue 3
Sensors, Vol 21, Iss 750, p 750 (2021)
ISSN: 1424-8220
DOI: 10.3390/s21030750
Popis: 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. Ministerio de Ciencia, Innovación y Universidades (España) | Ref. FPU16/03950 Universidad de Salamanca | Ref. Cátedra Iberdrola VIII Centenario
Databáze: OpenAIRE
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