Detection of humans in drone images using deep learning techniques

Interest in creating reliable and fast human detection systems has increased due to the growing use of Unmanned Aerial Vehicles (UAVs) in a variety of applications, including search and rescue operations and surveillance. The YOLOv8 deep learning algorithm, specially designed for drone imagery, is used in this paper's real-time human recognition method. Because it is effective and precise at identifying people in complicated backdrops and with different lighting conditions, YOLOv8, an enhanced version of the You Only Look Once (YOLO) object detection algorithm, is used. The YOLOv8 model is trained on a large dataset of annotated drone images, encompassing diverse scenarios and environments to facilitate robust human detection. This study uses a UAV Human Dataset encompassing various environmental conditions, ensuring robust performance in challenging scenarios. This study contributes to advancing drone-based technologies for human detection, offering a scalable and efficient solution for real-world deployment. The YOLOv8 model achieved a precision of 88.83%, recall of 71%, and mAP score of 74%.