Development of Thermal Principles for the Automation of the Thermographic Monitoring of Cultural Heritage

Abstract

The continuous deterioration of elements, with high patrimonial value over time, can only be mitigated or annulled through the application of techniques that facilitate the preventative detection of the possible agents of deterioration. InfraRed Thermography (IRT) is one of the most used techniques for this task. However, there are few IRT methodologies, which can automatically monitor the cultural heritage field, and are vitally important in eliminating the subjectivity in interpreting and accelerating the analysis process. In this work, a study is performed on a tessellatum layer of a mosaic to automatically: (i) Detect the first appearance of the thermal footprint of internal water, (ii) delimit the contours of the thermal footprint of internal water from its first appearance, and (iii) classify between harmful and non-harmful internal water. The study is based on the analysis of the temperature distribution of each thermal image. Five thermal images sequences are acquired during the simulation of different real situations, obtaining a set of promising results for the optimization of the thermographic inspection process, while discussing the following recommended steps to be taken in the study for future researches.

IRT and GPR Techniques for Moisture Detection and Characterisation in Buildings

“…It should be noted that all the steps are based on the fact that areas affected by moisture produce a Gaussian temperature distribution on the histogram of the thermal images, which is independent of the Gaussian temperature distribution presented by the unaltered zones. This assumption has been proved in previous papers published by the authors of this work [ 20 , 28 , 49 , 59 ]. In addition, the thermal images are automatically processed in all the steps.…”

“…The skew parameter consists of the degree of distortion of the distribution of a dataset regarding a symmetrical distribution, i.e., the farness or closeness to the zero skew value, which is the corresponding value for an ideal Gaussian bell. On the other hand, the kurtosis parameter analyses the tails in a data distribution with respect to the tails of a Gaussian bell, being zero kurtosis value if they have the same tails [ 49 ].…”

Automatic Defects Segmentation and Identification by Deep Learning Algorithm with Pulsed Thermography: Synthetic and Experimental Data

“…The main purposes of the preprocessing stage are to make defects more distinguishable from the cluttered background of the thermal sequence and to normalize the different noising conditions. The bilateral filtering and thresholding technique) were adapted from [19] for the preprocessed stage in this work so as to be the reference database. In step 1, the bilateral filtering removes the possible existing noise in the thermal image, while it preserves the edges of possible objects.…”

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

“…IRT presents the advantageous features that correspond to a Non-Destructive Testing (NDT) technique, i.e., (i) non-intrusion and non-damage to the integrity of the object as opposed to destructive techniques, and (ii) higher objectivity and speed compared to traditional methods [ 5 ]. In addition, IRT presents added advantageous features such as: (i) The non-contact with the heritage object, (ii) the real time operation, very important for cultural heritage monitoring, (iii) the ability to analyze any surface regardless of the type of object, (iv) the possibility to monitor many points of an object at the same time, (v) the capacity to perform large-scale studies of objects, (vi) the interpretation of the results in two and three dimensions [ 6 , 7 ], and (vii) the possibility of a qualitative and quantitative analysis [ 8 , 9 ].…”

“…It should be noted that PPT, TSR, and DTT allow for simultaneous qualitative and quantitative analysis, and that there are also self-developed IRT data processing algorithms based on thermal fundamentals for the automatic segmentation and characterization of defects in different heritage objects. This is the case with the IRT works of Garrido et al [ 8 , 29 , 30 ], focused on the surface moisture analysis in materials at different scales.…”