Obstacle avoidance in unmanned aerial vehicles using image segmentation and deep learning

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dc.contributor.authorObaid, Amro Ali
dc.contributor.authorKoyuncu, Hakan
dc.date.accessioned2022-12-23T10:35:28Z
dc.date.available2022-12-23T10:35:28Z
dc.date.issued2022en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Bilişim Teknolojileri Ana Bilim Dalıen_US
dc.description.abstractMachine learning is a branch of artificial intelligence based on the idea that systems can learn to identify patterns and make decisions with a minimum of human intervention. In this study, demonstration learning will be used, using neural networks in a prototype of a drone built to perform trajectories in controlled environments. To accelerate the training convergence process, a new training data selection approach has been introduced, which picks data from the experience pool based on priority instead of randomness. An autonomous maneuver strategy for dual-UAV olive formation air warfare is provided, which makes use of UAV capabilities such as obstacle avoidance, formation, and confrontation to maximize the effectiveness of the attack.en_US
dc.identifier.citationObaid, A. A., Koyuncu, H. (2022). Obstacle avoidance in unmanned aerial vehicles using image segmentation and deep learning. In 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 912-915). IEEE.en_US
dc.identifier.endpage915en_US
dc.identifier.isbn9781665470131
dc.identifier.scopus2-s2.0-85142828446
dc.identifier.scopusqualityN/A
dc.identifier.startpage912en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/3147
dc.indekslendigikaynakScopus
dc.institutionauthorObaid, Amro Ali
dc.institutionauthorKoyuncu, Hakan
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings
dc.relation.isversionof10.1109/ISMSIT56059.2022.9932865en_US
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLEACHen_US
dc.subjectPSOen_US
dc.subjectRoutingen_US
dc.subjectWSNen_US
dc.titleObstacle avoidance in unmanned aerial vehicles using image segmentation and deep learning
dc.typeConference Object

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