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Due to the environmental impacts caused by the construction industry,repurposing existing buildings and making them more energy-efficient has becomea high-priority issue. However, a legitimate concern of land developers isassociated with the buildings' state of conservation. For that reason, infraredthermography has been used as a powerful tool to characterize these buildings'state of conservation by detecting pathologies, such as cracks and humidity.Thermal cameras detect the radiation emitted by any material and translate itinto temperature-color-coded images. Abnormal temperature changes may indicatethe presence of pathologies, however, reading thermal images might not be quitesimple. This research project aims to combine infrared thermography and machinelearning (ML) to help stakeholders determine the viability of reusing existingbuildings by identifying their pathologies and defects more efficiently andaccurately. In this particular phase of this research project, we've used animage classification machine learning model of Convolutional Neural Networks(DCNN) to differentiate three levels of cracks in one particular building. Themodel's accuracy was compared between the MSX and thermal images acquired fromtwo distinct thermal cameras and fused images (formed through multisourceinformation) to test the influence of the input data and network on thedetection results.

Machine Learning and Thermography Applied to the Detection and Classification of Cracks in Building