YOLO-V3 based real-time drone detection algorithm

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dc.contributor.authorAlsanad, Hamid R.
dc.contributor.authorSadik, Amin Z.
dc.contributor.authorBayat, Oguz
dc.date.accessioned2022-04-21T10:56:55Z
dc.date.available2022-04-21T10:56:55Z
dc.date.issued2022en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesien_US
dc.description.abstractDrones are currently being used in a wide range of useful tasks that are too dangerous or/and expensive to be performed by humans. However, this is increasingly developing security breaching issues due to the possibility of misuse of unmanned aircraft in illegal activities such as drug smuggling, terrorism, etc. Thus, the detection and tracking of drones are becoming a crucial topic. Unfortunately, due to the drone’s small size, its detection methods are generally unreliable: high false alarm rate, low accuracy rate, and low detection speed are well-known aspects of this detection. The new emerging real-time algorithm based on the improved “You Only Look Once” (YOLO-V3) algorithm is proposed here for drone detection. This newly designed algorithm comprises multiple phases and has shown the potential to outperform the traditional detection approaches. The proposed algorithm enhances the performance of YOLO-V3 by designing and building a CNN to solve the problem of a large number of YOLO-V3 parameters, using densely connected modules to enhance the interlayer connection of CNNs and further strengthen the connection between dense neural network blocks, and finally improving the YOLO-V3 multiple-scale detection by expanding the three-scale to four-scale detection to increase the accuracy of detecting small objects like drones. The evaluation results of our algorithm obtain 96% on average precision and 95.60% accuracy.en_US
dc.identifier.citationAlsanad, H. R., Sadik, A. Z., Ucan, O. N., Ilyas, M., & Bayat, O. (2022). YOLO-V3 based real-time drone detection algorithm. Multimedia Tools and Applications, 1-14.en_US
dc.identifier.scopus2-s2.0-85127234303
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://hdl.handle.net/20.500.12939/2359
dc.identifier.wosWOS:000780464900004
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorUcan, Osman N.
dc.institutionauthorIlyas, Muhammad
dc.language.isoen
dc.publisherMultimedia Tools and Applicationsen_US
dc.relation.ispartofMultimedia Tools and Applications
dc.relation.isversionof10.1007/s11042-022-12939-4en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional Neural Networken_US
dc.subjectDenseen_US
dc.subjectDroneen_US
dc.subjectObject Detectionen_US
dc.subjectYOLO-V3en_US
dc.titleYOLO-V3 based real-time drone detection algorithm
dc.typeArticle

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