15,500 Seconds: Lean UAV Classification Using EfficientNet and Lightweight Fine-Tuning

• URL: http://arxiv.org/abs/2506.11049v4
• Date: Thu, 14 Aug 2025 14:50:40 GMT
• Title: 15,500 Seconds: Lean UAV Classification Using EfficientNet and Lightweight Fine-Tuning
• Abstract summary: This paper addresses the challenge of data scarcity in UAV audio classification by expanding on prior work.<n>We use a custom dataset of 3,100 UAV audio clips (15,500 seconds) spanning 31 distinct drone types.<n>Experiments were conducted with five-fold cross-validation, assessing accuracy, training efficiency, and robustness.
• Score: 2.3354223046061016
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: As unmanned aerial vehicles (UAVs) become increasingly prevalent in both consumer and defense applications, the need for reliable, modality-specific classification systems grows in urgency. This paper addresses the challenge of data scarcity in UAV audio classification by expanding on prior work through the integration of pre-trained deep learning models, parameter-efficient fine-tuning (PEFT) strategies, and targeted data augmentation techniques. Using a custom dataset of 3,100 UAV audio clips (15,500 seconds) spanning 31 distinct drone types, we evaluate the performance of transformer-based and convolutional neural network (CNN) architectures under various fine-tuning configurations. Experiments were conducted with five-fold cross-validation, assessing accuracy, training efficiency, and robustness. Results show that full fine-tuning of the EfficientNet-B0 model with three augmentations achieved the highest validation accuracy (95.95), outperforming both the custom CNN and transformer-based models like AST. These findings suggest that combining lightweight architectures with PEFT and well-chosen augmentations provides an effective strategy for UAV audio classification on limited datasets. Future work will extend this framework to multimodal UAV classification using visual and radar telemetry.

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