@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}

}

@article{lapointe_literature_2020,

title = {A Literature Review of AR-Based Remote Guidance Tasks with User Studies},

author = {J. -F. Lapointe and H. Molyneaux and M. S. Allili},

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

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089165019&doi=10.1007%2f978-3-030-49698-2_8&partnerID=40&md5=7af5345630a6dc4fc14e46a4ee1b1fdc},

doi = {10.1007/978-3-030-49698-2_8},

issn = {03029743},

year  = {2020},

date = {2020-01-01},

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

volume = {12191 LNCS},

pages = {111–120},

abstract = {The future of work is increasingly mobile and distributed across space and time. Institutions and individuals are phasing out desktops in favor of laptops, tablets and/or smart phones as much work (assessment, technical support, etc.) is done in the field and not at a desk. There will be a need for systems that support remote collaborations such as remote guidance. Augmented reality (AR) is praised for its ability to show the task at hand within an immersive environment, allowing for spatial clarity and greater efficiency, thereby showing great promise for collaborative and remote guidance tasks; however, there are no systematic reviews of AR based remote guidance systems. This paper reviews the literature describing AR-based remote guidance tasks and discusses the task settings, technical requirements and user groups within the literature, followed by a discussion of further areas of interest for the application of this technology combined with artificial intelligence (AI) algorithms to increase the efficiency of applied tasks. © 2020, NRC Canada.},

note = {ISBN: 9783030496975 

Publisher: Springer},

keywords = {Artificial intelligence, augmented reality, Efficiency, Future of works, Human computer interaction, Immersive environment, Literature reviews, Mixed reality, Remote collaboration, Remote guidance, Smartphones, systematic review, Technical requirement, Technical support},

pubstate = {published},

tppubtype = {article}

}