Sustainable Drone Detection using Deep Learning Paradigms

Abstract

Drones are becoming more and more common, which has advantages but also concerns. They can be used to support illegal operations like drug trafficking and endanger places that are important to security. While advances in sensor technology haven't produced reliable answers in the literature, current drone detection and neutralization methods frequently require previous detection and categorization. Using radio frequency (RF) signals and a frequency signature-based deep learning model, this work promotes an environmentally friendly and multidisciplinary method of drone identification and categorization. Using commercial drones, we generated a novel drone RF dataset and thoroughly evaluated two-stage and combined detection-classification frameworks, assessing their efficacy in simultaneous multi-signal and single-signal scenarios. According to our investigation, the Deep Residual Neural Network (DRNN) framework achieves classification performance that is equivalent to that of the YOLOV5 framework, but it offers improved detection performance in multi-signal settings when compared to traditional Goodness-of-Fit (GoF) spectrum sensing. This study opens the door for multidisciplinary efforts to address new security concerns by showcasing the possibility of long-term, data-driven drone detection methods.