@article{lapointe_ai-ar_2022,

title = {AI-AR for Bridge Inspection by Drone},

author = {J. -F. Lapointe and M. S. Allili and L. Belliveau and L. Hebbache and D. Amirkhani and H. Sekkati},

editor = {Fragomeni G. Chen J.Y.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131961739&doi=10.1007%2f978-3-031-06015-1_21&partnerID=40&md5=f57dfc1d9207b936684f18893eb5bfa7},

doi = {10.1007/978-3-031-06015-1_21},

issn = {03029743},

year  = {2022},

date = {2022-01-01},

journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},

volume = {13318 LNCS},

pages = {302–313},

abstract = {Good and regular inspections of transportation infrastructures such as bridges and overpasses are necessary to maintain the safety of the public who uses them and the integrity of the structures. Until recently, these inspections were done entirely manually by using mainly visual inspection to detect defects on the structure. In the last few years, inspection by drone is an emerging way of achieving inspection that allows more efficient access to the structure. This paper describes a human-in-the-loop system that combines AI and AR for bridge inspection by drone. © 2022, Springer Nature Switzerland AG.},

note = {ISBN: 9783031060144 

Publisher: Springer Science and Business Media Deutschland GmbH},

keywords = {AR, augmented reality, Bridge inspection, Bridges, Deep learning, Drone, Drones, Human-in-the-loop, Inspection, Regular inspections, Remote guidance, RPAS, Transportation infrastructures, Visual inspection},

pubstate = {published},

tppubtype = {article}

}