Performance Evaluation of YOLOv4 for Instant Object Detection in UAVs
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Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Unmanned Aerial Vehicles (UAVs) have become integral in various research domains due to the advantages they provide. Current UAV systems rely on Global Navigation Satellite Systems (GNSS) for flight control and sensors for obstacle detection, yet fully autonomous decision-making remains a challenge. This study evaluates the performance of YOLOv4, a Convolutional Neural Network (CNN) image recognition algorithm, for instantaneous object detection and classification in UAV-captured aerial images. The study demonstrates the applicability of YOLOv4 in real-time object detection and classification through UAV image feeds. The proposed approach advances the understanding of deploying CNNs in UAVs, offering a cost-effective solution for real-time object detection and classification, essential for autonomous UAV operations.
Açıklama
32nd IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2024--AG289 Tarsus Univ Campus, Mersin, TURKEY
Anahtar Kelimeler
object detection, YOLOv4, UAV
Kaynak
32nd Ieee Signal Processing and Communications Applications Conference, Siu 2024
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
