Title:
Use of Machine Learning to Create a Database of Wires for Helicopter Wire Strike Prevention
Use of Machine Learning to Create a Database of Wires for Helicopter Wire Strike Prevention
| dc.contributor.author | Harris, Caleb M. | |
| dc.contributor.author | Achour, Gabriel | |
| dc.contributor.author | Payan, Alexia P. | |
| dc.contributor.author | Mavris, Dimitri N. | |
| dc.contributor.corporatename | Georgia Institute of Technology. Aerospace Systems Design Laboratory | en_US |
| dc.contributor.corporatename | American Institute of Aeronautics and Astronautics | |
| dc.contributor.corporatename | Georgia Institute of Technology. Aerospace Systems Design Laboratory | |
| dc.date.accessioned | 2021-01-21T01:32:12Z | |
| dc.date.available | 2021-01-21T01:32:12Z | |
| dc.date.issued | 2021-01-04 | |
| dc.description | AIAA Scitech 2021 Forum. | en_US |
| dc.description.abstract | Rotorcraft collisions with wires and power lines have been a major cause of accidents over the past decades. They are rather difficult to predict and often result in fatalities. For this reason, there is a push to provide pilots with additional information regarding wires in the surrounding environment of the helicopter. However, the precise locations of power lines and other aerial wires are not available in any centralized database. This work proposes the development of a wire database in two phases. First, power line structures are detected from aerial imagery using deep learning techniques. Second, the complete power grid network is predicted using a centralized many-to-many graph search. The two-step framework produces an approximate medium-voltage grid stored as a set of connected line segments in GPS coordinates. Experiments are conducted in Washington D.C. using openly available datasets. Results show that utility pole locations can be predicted from satellite imagery using deep learning methods and a full grid network can be generated to a level of detail depending on computational power and available data for inference in the graph search. Even with limited computational resources and a noisy dataset, over a a fourth of the grid network is directly predicted within a range of seven meters, and the majority of the network is visually inferred from nearby detections. Moving forward, the goal is to apply the proposed framework to larger regions of the U.S., with rural and urban environments, to map all wires and cables that are a threat to rotorcraft safety. | en_US |
| dc.identifier.citation | Caleb M. Harris, Gabriel Achour, Alexia P. Payan and Dimitri N. Mavris. Use of Machine Learning to Create a Database of Wires for Helicopter Wire Strike Prevention. AIAA Scitech 2021 Forum. AIAA 2021-0527. January 2021. | en_US |
| dc.identifier.doi | https://doi.org/10.2514/6.2021-0527 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1853/64233 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.publisher | Georgia Institute of Technology | |
| dc.publisher.original | American Institute of Aeronautics and Astronautics | en_US |
| dc.publisher.original | American Institute of Aeronautics and Astronautics (AIAA) | |
| dc.relation.ispartofseries | ASDL; | en_US |
| dc.subject | Deep learning | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Many-to-many Dijkstra | en_US |
| dc.subject | Rotorcraft safety | en_US |
| dc.subject | Wire strike | en_US |
| dc.title | Use of Machine Learning to Create a Database of Wires for Helicopter Wire Strike Prevention | en_US |
| dc.type | Text | |
| dc.type.genre | Paper | |
| dspace.entity.type | Publication | |
| local.contributor.author | Payan, Alexia P. | |
| local.contributor.author | Mavris, Dimitri N. | |
| local.contributor.corporatename | Daniel Guggenheim School of Aerospace Engineering | |
| local.contributor.corporatename | Aerospace Systems Design Laboratory (ASDL) | |
| local.contributor.corporatename | College of Engineering | |
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