Building Façade Protection Using Spatial and Temporal Deep Learning Models Applied to Thermographic Data. Laboratory Tests †

Abstract

This paper proposes a methodology that combines spatial and temporal deep learning (DL) models applied to data acquired by InfraRed Thermography (IRT). The data were acquired from laboratory specimens that simulate building façades. The spatial DL model (Mask Region-Convolution Neural Network, Mask R-CNN) is used to identify and classify different artificial subsurface defects, whereas the temporal DL model (Gated Recurrent Unit, GRU) is utilized to estimate the depth of each defect, all in an autonomous and automated manner. An F-score average of 92.8 ± 5.4% regarding defect identification and classification, and a root-mean-square error equal to 1 mm in the estimation of defect depth equal to 10 mm as the best defect depth estimation, are obtained with this first application of a combination of spatial and temporal DL models to the IRT inspection of buildings.