@inproceedings{lapointe_field_2025,

title = {Field Trials of an AI-AR-Based System for Remote Bridge Inspection by Drone},

author = {J. -F. Lapointe and M. S. Allili and N. Hammouche},

editor = {Harris D. and Li W.-C. and Krömker H.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213387549&doi=10.1007%2f978-3-031-76824-8_20&partnerID=40&md5=565ae5dded9cfdf27632e79e702c7718},

doi = {10.1007/978-3-031-76824-8_20},

isbn = {03029743 (ISSN); 978-303176823-1 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Lect. Notes Comput. Sci.},

volume = {15381 LNCS},

pages = {278–287},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Bridge inspections are important to ensure the safety of users of these critical transportation infrastructures and avoid tragedies that could be caused by the collapse of these infrastructures. This paper describes the results of field trials of an advanced system for remotely guided inspection of bridges by a drone, which relies on artificial intelligence and augmented reality to achieve it. Results indicate that a high speed network link is critical to achieve good performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},

note = {Journal Abbreviation: Lect. Notes Comput. Sci.},

keywords = {Advanced systems, Air navigation, Artificial intelligence, artificial intelligence (AI), augmented reality, augmented reality (AR), Bridge inspection, Concrete bridges, Drone, Drones, Field trial, HIgh speed networks, High-speed Networks, Network links, Performance, Remote guidance, Transportation infrastructures, UAV},

pubstate = {published},

tppubtype = {inproceedings}

}