<![CDATA[This study aims to develop an image detection system capable of identifying manned and unmanned aircraft objects to support air traffic surveillance. The increasing flight activity, both from commercial aircraft and drones, requires a more optimal surveillance system to connect the airspace efficiently. In this study, a Convolutional Neural Network (CNN) model utilizing the You Only Look Once version 5 (YOLOv5) method is employed to detect and classify objects in real-time from aircraft images. The methodology employed includes collecting aerial image data, labeling the data, and training object detection models using YOLOv5. The dataset used consists of 2,520 images of manned aircraft (warplanes) and 5,422 images of unmanned aircraft (drones). The experimental results demonstrate that the YOLOv5 model achieves high detection accuracy for both manned and unmanned aircraft, with a relatively fast inference time, thereby supporting the development of an effective air traffic surveillance system. This system is expected to be an integral part of a more sophisticated and responsive air traffic surveillance solution.]]>
Copyrights © 2025
