The article presents the results of research in the area of using deep neural networks to identify moisture inside the walls of buildings using electrical impedance tomography. Two deep neural networks were used to transform the input measurements into images of damp places - convolutional neural networks (CNN) and recurrent long short-term memory networks LSTM. After training both models, a comparative assessment of the results obtained thanks to them was made. The conclusions show that both models are highly utilitarian in the analyzed problem. However, slightly better results were obtained with the LSTM method.


machine learning; deep learning; electrical impedance tomography; moisture detection in walls

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Published : 2022-03-31

Kłosowski, G., & Rymarczyk, T. (2022). APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS IN WALL MOISTURE IDENTIFICATION BY EIT METHOD. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 12(1), 20-23.

Grzegorz Kłosowski
Lublin University of Technology, Faculty of Management  Poland
Tomasz Rymarczyk 
University of Economics and Innovation in Lublin, Institute of Computer Science and Innovative Technologies  Poland