Deep learning based object detection with unmanned aerial vehicle equipped with embedded system

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dc.contributor.authorKıraç, Ertuğrul
dc.contributor.authorÖzbek, Sunullah
dc.date.accessioned2025-02-09T14:33:06Z
dc.date.available2025-02-09T14:33:06Z
dc.date.issued2024
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Savunma Teknolojileri Ana Bilim Dalı
dc.description.abstractThis study aims to introduce an Unmanned Aerial Vehicle (UAV) platform capable of performing real-time object detection and classification tasks using computer vision techniques in the field of artificial intelligence. Previous scientific research reveals the utilization of two different methods for object detection and classification via UAVs. One of these methods involves transmitting the acquired UAV images to a ground control center for processing, whereafter the processed data is relayed back to the UAV. The other approach entails transferring images over the internet to a cloud system, where image processing is conducted, and the resultant data is subsequently sent back to the UAV. This allows the UAV to autonomously perform predefined tasks. Enabling the UAV with autonomous decision-making capabilities and the ability to perform object detection and classification from recorded images requires an embedded artificial intelligence module. The ability of the UAV to utilize image processing technologies through embedded systems significantly enhances its object detection and classification capabilities, providing it with a significant advantage. This enables the UAV to be used more effectively and reliably in various tasks. In the proposed approach, image processing was achieved by mounting a Raspberry Pi 4 and camera on the UAV. Additionally, a Raspberry Pi-compatible 4G/LTE modem kit was used to provide remote intervention capability, and the Coral Edge TPU auxiliary processor was used to increase object detection speed. The TensorFlow Library and the SSD MobilNetV2 convolutional neural network model were used for image processing. During test flights, accuracy values of approximately 96.3% for car detection and 96.2% for human detection were achieved.
dc.identifier.doi10.30518/jav.1356997
dc.identifier.endpage25
dc.identifier.issn2587-1676
dc.identifier.issue1
dc.identifier.startpage15
dc.identifier.urihttps://hdl.handle.net/11501/1759
dc.identifier.urihttps://doi.org/10.30518/jav.1356997
dc.identifier.volume8
dc.institutionauthorKıraç, Ertuğrul
dc.institutionauthorid0000-0001-6645-5444
dc.language.isoen
dc.publisherVedat Veli Çay
dc.relation.ispartofJournal of Aviation
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Öğrenci
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_DergiPark_20250209
dc.subjectUAV
dc.subjectDeep Learning
dc.subjectComputer Vision
dc.subjectConventional Neural Network
dc.subjectObject Detection
dc.titleDeep learning based object detection with unmanned aerial vehicle equipped with embedded system
dc.typeArticle

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