Saw-YOLOv5: Scale-Aware YOLOv5 for Object Detection in Aerial Images

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dc.contributor.authorDoruk, Abdullah Enes
dc.contributor.authorAlgul, Mucteba
dc.contributor.authorAkyurek, Feyzullah
dc.contributor.authorAlpaydm, Osman Kursat
dc.contributor.authorUslu, Fatmatulzehra
dc.date.accessioned2026-02-12T21:02:49Z
dc.date.available2026-02-12T21:02:49Z
dc.date.issued2023
dc.departmentBursa Teknik Üniversitesi
dc.description5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2023 -- 2023-06-08 through 2023-06-10 -- Istanbul -- 190025
dc.description.abstractThe detection of objects in aerial images is impor-tant for many real world problems related to military defense, transportation, and etc. However, this is a challenging task as a result of the presence of various scales of objects in the same image, the large variety of contexts across aerial images, various brightness levels due to image acquisition at different times of the day and so on. To address these challenges, this paper introduces Saw-YOLOv5 for object detection in aerial images. Saw-YOLOv5 is a deep network based on YOLOv5, which was proposed for object detection in natural images. Saw-YOLOv5 extends YOLOv5 with the addition of several attention modules in its design. The results of our experiments, conducted on the aerial dataset delivered by the Turkey Technology Team for the Artificial Intelligence in Transportation Competition, showed that Saw-YOLOv5 outperforms previous methods, particularly for pedestrian detection, by yielding a mean mAP of 80.23% over all objects. © 2023 IEEE.
dc.identifier.doi10.1109/HORA58378.2023.10155778
dc.identifier.isbn9798350337525
dc.identifier.scopus2-s2.0-85165688341
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/HORA58378.2023.10155778
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6552
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofHORA 2023 - 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.snmzKA_Scopus_20260212
dc.subjectaerial image analysis
dc.subjectattention mechanism
dc.subjectdeep networks
dc.subjectObject detection
dc.titleSaw-YOLOv5: Scale-Aware YOLOv5 for Object Detection in Aerial Images
dc.typeConference Object

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