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Real-time object detection using an ultra-high-resolution camera on embedded systems

Antonakakis Marios, Tzavaras Aimilios, Tsakos Konstantinos, Spanakis Emmanouil G., Sakkalis, Vangelis, Zervakis Michail, Petrakis Evripidis

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URIhttp://purl.tuc.gr/dl/dias/D56A1139-65C2-4E32-B5B4-64BB221CF0B3-
Identifierhttps://doi.org/10.1109/IST55454.2022.9827742-
Identifierhttps://ieeexplore.ieee.org/document/9827742-
Languageen-
Extent6 pagesen
TitleReal-time object detection using an ultra-high-resolution camera on embedded systemsen
CreatorAntonakakis Mariosen
CreatorΑντωνακακης Μαριοςel
CreatorTzavaras Aimiliosen
CreatorΤζαβαρας Αιμιλιοςel
CreatorTsakos Konstantinosen
CreatorΤσακος Κωνσταντινοςel
CreatorSpanakis Emmanouil G.en
CreatorSakkalis, Vangelisen
CreatorZervakis Michailen
CreatorΖερβακης Μιχαηλel
CreatorPetrakis Evripidisen
CreatorΠετρακης Ευριπιδηςel
PublisherInstitute of Electrical and Electronics Engineersen
DescriptionThe work has received funding from the European Union’s Horizon 2020 – Research and Innovation Framework Programme H2020-SU-SEC-2019, under Grant Agreement No 883272– BorderUAS.en
Content SummaryUnnamed Aerial Vehicle (UAV) - based remote sensing is a promising technology that is being applied for inspecting live scenes from high altitudes (e.g., for surveillance and recognizing emergencies). The evolution of hardware and software technologies in the last few years has generated additional interest in embedded systems research and its implementation in energy-independent UAVs for remote sensing. Alongside, ultra-high-resolution optical sensors are mandatory for acquiring high-resolution images which are necessary for accurate object detection from a distance (e.g., 1,000 meters). The processing of ultra-high-resolution images (e.g., 4K or 8K) is beyond the typical resolutions which are used for object detection (e.g., < 2K) emerging a necessity for special treatment in order to succeed a fast object detection. We propose a three-step approach deployed on a Docker runtime environment in an Nvidia Jetson AGX Xavier board. To support fast object detection, the captured images are split into K parts processed in parallel in separate containers running the YOLOv5 object detection algorithm. A final detection is constructed based on each one of the K detections. The experimental results are a good support to our claims of efficiency: the method can achieve close to real-time object detection for ultra-high (i.e., 8K) resolution images (i.e., in less than 1 second per frame).en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2024-08-28-
Date of Publication2022-
SubjectUltra-high-resolution imagesen
SubjectReal-time object detectionen
SubjectYOLOv5en
SubjectEmbedded systemsen
SubjectRemote sensingen
Bibliographic CitationM. Antonakakis, A. Tzavaras, K. Tsakos, E. G. Spanakis, V. Sakkalis, M. Zervakis, and E. G. M. Petrakis, "Real-time object detection using an ultra-high-resolution camera on embedded systems," in Proceedings of the 2022 IEEE International Conference on Imaging Systems and Techniques (IST 2022), Kaohsiung, Taiwan, 2022, doi: 10.1109/IST55454.2022.9827742.en

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