Abstract: Drones are increasingly being utilized for recreational purposes and across various fields such as engineering, disaster response, logistics, and airport security. However, their potential misuse has raised serious concerns regarding the safety and surveillance of critical infrastructures, particularly in airport environments. Incidents involving unauthorized drone activity have frequently disrupted airline operations in recent years. To mitigate this issue, this study proposes a novel deep learning-based approach for drone detection and recognition. The method demonstrates superior performance compared to existing systems by accurately identifying the presence of drones, distinguishing between two drone types, and differentiating them from birds, despite the visual and behavioral similarities that often confuse. This advancement significantly enhances aerial object classification and reinforces airspace security.
Key Words: drone; UAV; deep learning; convolutional neural network CNN; drone image dataset; drone detection; drone recognition.
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DOI:
10.17148/IJARCCE.2025.14560
