Drone detection on video data using convolutional neural networks

In this thesis study, it was aimed to detect unmanned aerial vehicles (drones) in real time by using image processing and deep learning-based approaches. The YOLOv11 model, which is the most recent version of the YOLO architecture, was used as the main model, and drone-bird classification was performed under different hyperparameter settings. The dataset used in training consists of both drone and bird images, and the images were manually labeled by extracting frames from video footage. During the training process, variables such as the freeze parameter and input size were tested systematically. According to the experimental results, the YOLOv11 model achieved effective object detection even in complex scenes with high precision and recall values. The findings of this study can contribute to the development of AI-assisted surveillance systems, especially in areas such as security, border control, and airspace monitoring. In addition, the performance of YOLOv11 was compared with the YOLOv8 model, and the experimental results revealed the advantages of YOLOv11 over the older version.