Keskinoglu, Gul BaharYesilkaya, Nimet SeleriBayraktar, IremKose, Ercan2025-03-172025-03-172024979-8-3503-8897-8979-8-3503-8896-12165-0608https://doi.org/10.1109/SIU61531.2024.10600728https://hdl.handle.net/20.500.13099/179532nd IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2024--AG289 Tarsus Univ Campus, Mersin, TURKEYUnmanned 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.trinfo:eu-repo/semantics/closedAccessobject detectionYOLOv4UAVConference Object10.1109/SIU61531.2024.10600728N/AWOS:0012978947000142-s2.0-85200867107N/A