UAV Detection With YOLOV4
Drones are becoming increasingly popular not only for recreational purposes but also in a variety of applications in engineering, disaster management, logistics, securing airports, and others. In addition to their useful applications, an alarming concern regarding the physical infrastructure security, safety, and surveillance at airports has arisen due to the potential of their use in mali-cious activities. In recent years, there have been many reports of unauthorized use of various types of drones at airports and disruption of airline operations. To address this problem, this study proposes a novel Deep Learning based method for efficient detection and recognition of two types of drones(helicopter and multirotor) and birds.
Different weather conditions and crowded background
Problems such as the presence of drones in crowded environments and different lighting conditions make drone detection difficult.
The resemblance of drones and birds
Drones can be mistaken for birds, especially at long distances, because of their similarity in behavior and physical characteristics.
Lack of Scalability
The presence of drones at close and distant distances challenges to accurately detect and recognize different types of drones.
Small size of drones at long distances
The presence of drones at long ranges makes them smaller and causes problems in detection and recognition.
METHOD
Considering the reviewed advantages of the YOLOv4 deep learning network, it is applied to detect flying drones and birds in crowded environments. The proposed Network consists of a four-section architecture as input, backbone, neck, and head.
Diagram
RESULTE
proposed model has the ability to distinguish birds and drones from each other and solve these challenges.
