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Submitted
November 11, 2025
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January 25, 2026
Published
June 1, 2026
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Abstract

Unmanned Aerial Vehicles (UAVs), or commonly known as drones, have been utilized in numerous applications, including civilian and military environments, due to their low cost and high level of usability. These devices have triggered significant security and protection issues, demanding the development of reliable detection and classification systems. However, UAVs have been used to achieve illegal operations, resulting in emerging threats to personal and public security. The traditional detection method suffers from small targets, uncertain situations, and stealthy cases. Consequently, due to their accuracy and efficacy in detecting drones in a diverse range of scenarios, Artificial Intelligence (AI) has become a significant solution for drone detection and classification. In this paper, an audio-based mechanism has been developed using a convolutional recurrent neural network (CRNN) algorithm to exploit the exclusive audio fingerprint of drones for detecting and identifying them. The model achieved 94.85% accuracy with a 94.41% F1-score from different types of drone sound files collected from the DADS site. It operates with a three-seed and thresholds to guarantee the stability and dependability of the model. This paper aims to validate the use of this algorithm for drone detection in an actual environment and train the model with different seeds to measure the model's stability against random variation.

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