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</o:shapelayout></xml><![endif]--></head><body lang=EL link=blue vlink=purple style='word-wrap:break-word'><div class=WordSection1><div><div><p class=MsoNormal style='margin-bottom:12.0pt'><b><span lang=EN-US>Date&Time</span></b><span lang=EN-US>: January 2022, 13th at 12:30pm CET [06:30 a.m. New-York] - [12:30 p.m. Paris] - [1:30 p.m. Thessaloniki] - [6:30 p.m. Beijing]<br><b>Title: </b>Drone Vision and Deep Learning for Infrastructure Inspection<br><b>Speaker:</b> Ioannis Pitas<br><br>To join the free 1-hour webinar, it is required to pre-register at,<br><a href="https://forms.gle/HZ9SAL9KXD2ReD9X6">https://forms.gle/HZ9SAL9KXD2ReD9X6</a><br>or through the journal website at,<br><a href="https://jivp-eurasipjournals.springeropen.com/">https://jivp-eurasipjournals.springeropen.com/</a><br>Contact: Jana Palinkas <a href="mailto:jana.palinkas@springernature.com"><jana.palinkas@springernature.com></a><br><br><b>Abstract:</b> This lecture overviews the use of drones for infrastructure inspection and maintenance. Various types of inspection, e.g., using visual cameras, LIDAR or thermal cameras are reviewed. Drone vision plays a pivotal role in drone perception/control for infrastructure inspection and maintenance, because: a) it enhances flight safety by drone localization/mapping, obstacle detection and emergency landing detection; b) performs quality visual data acquisition, and c) allows powerful drone/human interactions, e.g., through automatic event detection and gesture control. The drone should have: a) increased multiple drone decisional autonomy and b) improved multiple drone robustness and safety mechanisms (e.g., communication robustness/safety, embedded flight regulation compliance, enhanced crowd avoidance and emergency landing mechanisms). Therefore, it must be contextually aware and adaptive. Drone vision and machine learning play a very important role towards this end, covering the following topics: a) semantic world mapping b) drone and target localization, c) drone visual analysis for target/obstacle/crowd/point of interest detection, d) 2D/3D target tracking. Finally, embedded on-drone vision (e.g., tracking) and machine learning algorithms are extremely important, as they facilitate drone autonomy, e.g., in communication-denied environments.Primary application area is electric line inspection. Line detection and tracking and drone perching are examined. Human action recognition and co-working assistance are overviewed.<br>The lecture will offer : a) an overview of all the above plus other related topics and will stress the related algorithmic aspects, such as: b) drone localization and world mapping, c) target detection d) target tracking and 3D localization e) gesture control and co-working with humans. Some issues on embedded CNN and fast convolution computing will be overviewed as well.<br><br><b>Short bio:</b> Prof. Ioannis Pitas (IEEE fellow, IEEE Distinguished Lecturer, EURASIP fellow) received the Diploma and PhD degree in Electrical Engineering, both from the Aristotle University of Thessaloniki (AUTH), Greece. Since 1994, he has been a Professor at the Department of Informatics of AUTH and Director of the Artificial Intelligence and Information Analysis (AIIA) lab. He served as a Visiting Professor at several Universities.<br>His current interests are in the areas of computer vision, machine learning, autonomous systems, intelligent digital media, image/video processing, human- centred computing, affective computing, 3D imaging and biomedical imaging. He has published over 920 papers, contributed in 45 books in his areas of interest and edited or (co-)authored another 11 books. He has also been member of the program committee of many scientific conferences and workshops. In the past he served as Associate Editor or co-Editor of 13 international journals and General or Technical Chair of 5 international conferences. He delivered 98 keynote/invited speeches worldwide. He co-organized 33 conferences and participated in technical committees of 291 conferences. He participated in 71 R&D projects, primarily funded by the European Union and is/was principal investigator in 43 such projects. Prof. Pitas lead the big European H2020 R&D project MULTIDRONE: <a href="https://multidrone.eu/">https://multidrone.eu/</a>. He is AUTH principal investigator in H2020 R&D projects Aerial Core and AI4Media. He was chair and initiator of the Autonomous Systems Initiative <a href="https://ieeeasi.signalprocessingsociety.org/">https://ieeeasi.signalprocessingsociety.org/</a>. He is chair of the International AI Doctoral Academy (AIDA) <a href="https://www.i-aida.org/">https://www.i-aida.org/</a> and is PI in Horizon2020 EU funded R&D projects AI4Media (1 of the 4 AI flagship projects in Europe) and AerialCore. He has 34400+ citations to his work and h-index 87+.<o:p></o:p></span></p></div></div></div><div id="DAB4FAD8-2DD7-40BB-A1B8-4E2AA1F9FDF2"><br />
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