Improved YOLOv4 for aerial object detection

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dc.authorid0000-0002-6842-1528
dc.authorid0000-0003-0779-9620
dc.contributor.authorAli, Sharoze
dc.contributor.authorSiddique, Arslan
dc.contributor.authorAteş, Hasan Fehmi
dc.contributor.authorGüntürk, Bahadır Kürşat
dc.date.accessioned2021-08-13T07:28:26Z
dc.date.available2021-08-13T07:28:26Z
dc.date.issued2021
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü
dc.description.abstractDrones equipped with cameras are being used for surveillance purposes. These surveillance systems need vision-based object detection of ground objects which look very small because of the altitude of drones. We propose an improved YOLOv4 model targeted for vision-based small object detection. We investigated the performance of state of the art YOLOv4 object detector on the VisDrone dataset. We enhanced the features of small objects by connecting Upsampling layers and concatenating the upsampled features with the original features to obtain more refined and grained features for small objects. Experiments showed that the modified YOLOv4 achieved 2 percent better mAP results as compared to the original YOLOv4 at different image resolutions on the VisDrone dataset while running at the same speed as the original YOLOv4.
dc.identifier.citationAli, S., Siddique, A., Ateş, H. F. ve Güntürk, B. K. (2021). Improved YOLOv4 for aerial object detection. 29th IEEE Conference on Signal Processing and Communications Applications, SIU. Virtual, Istanbul, 9-11 June 2021. https://dx.doi.org/10.1109/SIU53274.2021.9478027
dc.identifier.doi10.1109/SIU53274.2021.9478027
dc.identifier.isbn9781665436496
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://dx.doi.org/10.1109/SIU53274.2021.9478027
dc.identifier.urihttps://hdl.handle.net/20.500.12511/7811
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof29th IEEE Conference on Signal Processing and Communications Applications, SIUen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectDeep Learning
dc.subjectObject Detection
dc.subjectSmall Object
dc.titleImproved YOLOv4 for aerial object detection
dc.typeConference Object

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