Saw-YOLOv5: Scale-Aware YOLOv5 for Object Detection in Aerial Images
Küçük Resim Yok
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
Özet
The 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.
Açıklama
5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2023 -- 2023-06-08 through 2023-06-10 -- Istanbul -- 190025
Anahtar Kelimeler
aerial image analysis, attention mechanism, deep networks, Object detection
Kaynak
HORA 2023 - 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings
WoS Q Değeri
Scopus Q Değeri
N/A
