The increased use of Unmanned Aerial Vehicles (UAVs) in a variety of applications, including illegal activities such as drug trafficking and terrorism, has raised concerns about misuse. To protect special areas against illicit drones operations, UAVs' reconnaissance systems offer a promising solution. Having similar objects in the sky, like birds and airplanes, makes it difficult to detect these UAVs. In this work, a vision-based object recognition system, that uses a deep learning network called You Only Look Once version 5 (YOLOv5), is proposed to recognize drones and distinguish them from birds. The trained model is assessed using a set of performance measures such as mean Average Precision (mAP), precision, recall, F1-score and drone's location detection. A comparison with the YOLOv4 tool is carried out to show the effectiveness and superiority of the proposed YOLOv5 network.
Vision-Based Drone Recognition Using a YOLOv5-like Deep Learning Network
12.10.2023
492753 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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