Technical Diagnostics and Non-Destructive Testing, 2025, №02

2025 №02 (06)

DOI of Article 10.37434/tdnk2025.02.01

2025 №02 (02)

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"Tekhnichna Diahnostyka ta Neruinivnyi Kontrol" (Technical Diagnostics and Non-Destructive Testing) #2, 2025, pp. 3-11

Enhancing large-scale structure diagnostics through uav-based data and neural network analysis

L.M. Lobanov, I.L. Shkurat, D.I. Stelmakh, O.P. Shutkevych, V.V. Savitsky

E.O. Paton Electric Welding Institute of the NAS of Ukraine 11 Kazymyr Malevych Str., 03150, Kyiv, Ukraine. E-mail: innashkurat2909@gmail.com

The article presents an approach to remote diagnostics of damage to large-sized engineering structures using unmanned aerial vehicles (UAVs) and convolutional neural networks. The study was conducted to automate the process of detecting structural defects in the Kyiv TV tower. The research methodology involved the collection and preprocessing of 14187 images and the development of a modified architecture of the U-Net neural network for damage segmentation. An experimental study of different architectural settings of the model demonstrated the effectiveness of the proposed modifications, which reduced the error of defect detection by 3...5 % compared to the baseline models. It was found that the optimal number of training iterations is 15–20 epochs. The developed model demonstrated the ability to detect damage that may be missed by the operator, which confirms the potential of automated diagnostic systems based on artificial intelligence. The study provides new prospects for improving the efficiency of monitoring infrastructure facilities, especially in conditions of limited access or increased risks to personnel. 41 Ref., 2 Tabl., 6 Fig.
Keywords: remote diagnostics, defects, artificial intelligence, neural networks, image segmentation, UAVs

Received: 06.03.25
Received in revised form: 04.04.25
Accepted: 08.05.25

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