Power Line Segmentation in Aerial Images Using Convolutional Neural Networks


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Gidde, PS (2019) Power Line Segmentation in Aerial Images Using Convolutional Neural Networks. In: 8th International Conference on Pattern Recognition and Machine Intelligence, Dec. 17-20, 2019, Tezpur University, Tezpur.

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Visual inspection of transmission and distribution networks is often carried out by Various electricity companies on a regular basis to maintain the reliability, availability, and sustainability of electricity supply. Till date the widely used technique for carrying out an inspection is done manually either using foot patrol and/or helicopter operated manually. However, recently due to the widespread use of quadcopters/UAVs powered by deep learning algorithms, there have been requirements to automate the visual inspection of the power lines. With this objective in mind, this paper presents an approach towards automatic autonomous vision-based power line segmentation in optical images captured by Unmanned Aerial Vehicle (UAV) using deep learning backbone for data analysis. Power line segmentation is often considered as a first step required for power line inspection. Different state-of-the-art semantic segmenta- tion techniques available in the literature have been used and a comparative analysis has been done in terms of Jaccard index on two different power line databases. This paper also presents a new power line database captured using UAV along with the baseline results. Experimental results show that out of the four deep learning based segmentation architectures used in our experiments the Nested-UNet architecture out-performed others in terms of line segmentation accuracy in various background scenarios

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: UNet, Nested-UNet, Transfer learning, Unmanned Aerial Vehicle (UAV), Semantic segmentation, Power line inspection.
Subjects: Semiconductor Devices > Sensors and Nanotechnology
Divisions: Semiconductor Devices
Depositing User: Mr. Jitendra Nath Bajpai
Date Deposited: 27 Aug 2021 07:02
Last Modified: 27 Aug 2021 07:02
URI: http://ceeri.csircentral.net/id/eprint/479

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