Porting Computer Vision Models to the Edge for Smart City Applications: Enabling Autonomous Vision-Based Power Line Inspection at the Smart Grid Edge for Unmanned Aerial Vehicles (UAVs)
Smart grid infrastructure must be monitored and inspected - especially when subject to harsh operating conditions in extreme, remote environments such as the highlands of Iceland. Current methods for monitoring such critical infrastructure includes manual inspection, static video analysis (where con...
| Published in: | Proceedings of the Annual Hawaii International Conference on System Sciences, Proceedings of the 55th Hawaii International Conference on System Sciences |
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| Main Authors: | , |
| Format: | Text |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10125/80271 https://doi.org/10.24251/HICSS.2022.929 |
| Summary: | Smart grid infrastructure must be monitored and inspected - especially when subject to harsh operating conditions in extreme, remote environments such as the highlands of Iceland. Current methods for monitoring such critical infrastructure includes manual inspection, static video analysis (where connectivity is available) and unmanned aerial vehicle (UAV) inspection. UAVs offer certain inspection efficiencies; however, challenges persist given the time and UAV operator skill required. Collaborating with Landsnet, the Icelandic smart grid operator, we apply convolutional neural networks for image processing to detect smart grid transmission infrastructure and modify the resulting computer vision (CV) model to function on the edge of a UAV. In doing so, we overcome significant edge processing barriers. Our real-time CV model delivers decision insight on the UAV edge and enables autonomous flight path planning for use in smart grid inspection. Our approach is transferable to other smart city applications that could benefit from edge-based monitoring and inspection. |
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