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MELETI: A Machine-Learning-Based Embedded System Architecture for Infrastructure Inspection with UAVs

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Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing

Abstract

Telecommunication and power infrastructures are located in hard to reach environments and exposed to extreme weather conditions. As such, dedicated personnel is set to conduct inspections on a regular basis to maintain the quality of these critical infrastructure networks. However, these inspections are costly and usually pose significant risks to personnel. Hence, UAVs have found flourishing ground in the area of infrastructure inspection. In this chapter, we introduce UAV inspection requirements and propose MELETI, a system architecture that investigates how UAVs can be used to enable autonomous infrastructure inspection through innovative intelligent systems and machine learning algorithms. Finally, several infrastructure inspection challenges that implement MELETI are introduced on both power and telecommunication infrastructure inspection.

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Acknowledgements

This work has been supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 739551 (KIOS CoE) and from the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development. We would like to thank Electricity Authority of Cyprus (EAC) and Cyprus Telecommunication Authority (CYTA) for providing the locations to acquire data used in the present study, Antreas Anastasiou and Petros Petrides for assisting in data acquisition.

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Authors and Affiliations

  1. KIOS Research and Innovation Center of Excellence and the Department of Electrical and Computer Engineering, University of Cyprus, Nicosia, Cyprus

    Marios Pafitis, Antonis Savva, Christos Kyrkou, Panayiotis Kolios & Theocharis Theocharides

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  1. Marios Pafitis
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  2. Antonis Savva
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  5. Theocharis Theocharides
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Correspondence to Theocharis Theocharides .

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Editors and Affiliations

  1. Colorado State University, Fort Collins, CO, USA

    Sudeep Pasricha

  2. New York University Abu Dhabi, Abu Dhabi, Abu Dhabi, United Arab Emirates

    Muhammad Shafique

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Pafitis, M., Savva, A., Kyrkou, C., Kolios, P., Theocharides, T. (2024). MELETI: A Machine-Learning-Based Embedded System Architecture for Infrastructure Inspection with UAVs. In: Pasricha, S., Shafique, M. (eds) Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-40677-5_12

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  • DOI: https://doi.org/10.1007/978-3-031-40677-5_12

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