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Pylon Anti-Vandalism Monitoring System using Machine Learning Approach

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dc.contributor.author Ahuna, Mary
dc.contributor.author Muumbo, Alex
dc.contributor.author McLean, Jack
dc.date.accessioned 2022-02-09T08:35:22Z
dc.date.available 2022-02-09T08:35:22Z
dc.date.issued 2020
dc.identifier.issn 978-1-7281-6746-6
dc.identifier.uri http://hdl.handle.net/123456789/1903
dc.description Journal Article en_US
dc.description.abstract A country’s economic growth is largely dependent on the stability of its energy sector. The growth of Kenya’s power sector is greatly supported by Vision 2030 development blueprint, which is geared at transforming the country into a “middle income country that is able to provide a high quality of life to all of its citizens by the year 2030”. Though major investments have so far been made in the generation and transmission of power, the distribution sub-sector is yet to reap the full benefits due to a number of challenges ranging from financial to operation and maintenance. Transformers are key components of any power distribution network and despite their specified long operational life, they often fail within a short period of commissioning due to various reasons including vandalism. This has led to great losses to the power distribution sub-sector and consumers alike. This paper proposes an anti-theft system that uses machine learning technique to monitor the pylon’s environs against vandals. Results of the study reveals that the system is capable of detecting human images with confidence levels between 68% and 97%. en_US
dc.description.sponsorship IEEE en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject Human image, en_US
dc.subject Machine learning, en_US
dc.subject Object detection, en_US
dc.subject Pylon, en_US
dc.subject Transformer, en_US
dc.subject Vandalism en_US
dc.title Pylon Anti-Vandalism Monitoring System using Machine Learning Approach en_US
dc.type Article en_US


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