The Cutting-Edge YOLO11 for Advanced Aircraft Detection in Synthetic Aperture Radar (SAR) Imagery
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Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This study presents one of the first evaluations of the YOLO11 algorithm and is the first to apply it for aircraft detection from SAR images. A dataset combining SAR Aircraft Detection Dataset (SADD) images and additional web-mined images was used to train and test the model. YOLO11 achieved significant improvements in detection accuracy, with higher precision, recall, and mAP compared to earlier YOLO iterations such as YOLOv5, YOLOv8, YOLOv9, and YOLOv10. The model exhibited a balanced performance by maintaining competitive inference times while minimizing both false positives and missed detections. These results demonstrate the potential of YOLO11 for real-time applications, particularly in UAV-based surveillance systems, where both speed and accuracy are critical. © 2024 IEEE.
Açıklama
8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024 -- 6 December 2024 through 7 December 2024 -- Istanbul -- 206312
Anahtar Kelimeler
aircraft detection, CNN, deep learning, SAR imagery, YOLO11
Kaynak
8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024 - Proceedings
WoS Q Değeri
Scopus Q Değeri
N/A
