Performance evaluation of deep learning algorithms for heat loss damage classification in buildings from UAV-borne infrared images

Abstract

Deep learning (DL) algorithms are gaining popularity to automate anomaly detection in a variety of civil and mechanical engineering domains. Nevertheless, very few attempts have been made to use infrared (IR) images with DL algorithms to detect heat loss in a building and expedite the otherwise cumbersome energy audit process. However, no clear guidelines exist for the selection of appropriate algorithms or hyperparameters (e.g., number of epochs, learning rate, number of images) to implement DL with IR images. To address the research gap, a comparative sensitivity analysis of four well-known DL algorithms for the classification of heat losses using only IR images was performed for the first time. Four DL algorithms, namely, general convolutional neural network (CNN), VGG16, transfer learning (TL) VGG16, and TL inceptionV3 were selected and used for automating the detection of heat loss from IR images. The sensitivity of the selected CNN algorithms was analyzed based on i) the size of the IR images dataset, ii) the number of epochs, and iii) the effect of an unbalanced dataset. The goal was to provide a framework that may aid in the selection of suitable CNN architecture as well as parameters related to data size and an optimal number of training iterations for reliable heat loss identification using IR images. This article has provided a framework for automated heat loss detection that can be applied to any IR dataset without the need to pre-process the IR images. The accuracy of the selected DL algorithms is validated with IR images of laboratory setups and real-world buildings characterized by different types of defects. The comparative sensitivity analysis was conducted based on multiple performance indicators such as precision, recall, and F-1 score which resulted in optimal values of hyperparameters and the most suitable DL algorithm. The automated identification of heat loss yielded performance indicator values of more than 90%.