ALGORITHMS FOR THE AUTOMATIC DETECTION AND CHARACTERIZATION OF PATHOLOGIES IN HERITAGE ELEMENTS FROM THERMOGRAPHIC IMAGES
- 1Applied Geotechnologies Research Group, Mining & Energy Engineering School, University of Vigo, Rúa Maxwell s/n, 36310 Vigo, Spain
- 2TIDOP Research Group, EPS Ávila, University of Salamanca, Calle Hornos Caleros 50, 05003 Ávila, Spain
- 3Department of Industrial and Information Engineering and Economics (DIIIE), University of L'Aquila, Piazzale E. Pontieri 1, I-67100 Monteluco di Roio, L'Aquila, Italy
- 4Defense University Center, Spanish Naval Academy, Plaza de España s/n, 36920 Marín, Spain
Keywords: Heritage, conservation, moisture, InfraRed Thermography, automation, image processing
Abstract. Heritage elements, from historic buildings to stone sculptures and panels, stand as key elements in the history of humanity. Unfortunately, the deterioration of both the surface and the interior of these elements is inevitable, endangering the quality and existence of these structures of high historical value in the event of a delay in the implementation of the required maintenance tasks. InfraRed Thermography, IRT, appears as one of the most recent techniques to detect and characterize possible pathologies in structures in their early stages, being very useful for a preventive analysis in heritage elements.
This paper presents a methodology for the automatic detection and characterization of one of the most severe and frequent pathologies in heritage structures, moisture, from thermal images. The proposal stands as a demonstration of the potential of the IRT technique for heritage conservation applications, and as a new step towards the automation of the inspection process and optimization of the decision-taking in conservation actions within cultural heritage. For that, two thermal criteria and a semi-automatic image rectification process are implemented as main phases of the methodology, obtaining good results for the detection of moisture zones and accurate area values with regard to the real dimensions of each moisture zone. Specifically, an F-score average of 78 % ± 19 % regarding detection performance and a percentage relative error of minimum 4 %, and maximum of 12 %, referred to the area computation in unit metrics are obtained.