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Machine learning for ARWs
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-8235-2728
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-7631-002x
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-8870-6718
2023 (English)In: Aerial Robotic Workers: Design, Modeling, Control, Vision, and Their Applications / [ed] George Nikolakopoulos, Sina Sharif Mansouri, Christoforos Kanellakis, Elsevier, 2023, p. 159-174Chapter in book (Other academic)
Abstract [en]

Navigation in underground mine environments is a challenging area for the aerial robotic workers. Mines usually have complex geometries, including multiple crossings with different tunnels. Moreover, improving the safety of mines requires drones to be able to detect human workers. Thus, in this Chapter, we introduce frameworks for junction and human detection in the underground mine environments.

Place, publisher, year, edition, pages
Elsevier, 2023. p. 159-174
Keywords [en]
CNN, Human detection, Junction recognition, Transfer learning
National Category
Other Civil Engineering
Research subject
Robotics and Artificial Intelligence
Identifiers
URN: urn:nbn:se:ltu:diva-97392DOI: 10.1016/B978-0-12-814909-6.00016-0Scopus ID: 2-s2.0-85150104596ISBN: 978-0-12-814909-6 (print)OAI: oai:DiVA.org:ltu-97392DiVA, id: diva2:1758927
Available from: 2023-05-24 Created: 2023-05-24 Last updated: 2023-09-15Bibliographically approved

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Koval, AntonMansouri, Sina SharifKanellakis, Christoforos

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