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Advance and Refinement: The Evolution of UAV Detection and Classification Technologies

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arxiv 2409.05985 v1 pith:57NICQ2O submitted 2024-09-09 cs.CV eess.SP

Vladislav Semenyuk , Ildar Kurmashev , Alberto Lupidi , Dmitriy Alyoshin , Liliya Kurmasheva , Alessandro Cantelli-Forti This is my paper · ORCID
classification cs.CVeess.SP
keywords detectionclassificationaccuracyreviewsystemstechnologiesacousticadditionally
verification ladder T0 review T1 audit T2 compute T3 formal
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This review provides a detailed analysis of the advancements in unmanned aerial vehicle (UAV) detection and classification systems from 2020 to today. It covers various detection methodologies such as radar, radio frequency, optical, and acoustic sensors, and emphasizes their integration via sophisticated sensor fusion techniques. The fundamental technologies driving UAV detection and classification are thoroughly examined, with a focus on their accuracy and range. Additionally, the paper discusses the latest innovations in artificial intelligence and machine learning, illustrating their impact on improving the accuracy and efficiency of these systems. The review concludes by predicting further technological developments in UAV detection, which are expected to enhance both performance and reliability.

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  1. 15,500 Seconds: Lean UAV Classification Using EfficientNet and Lightweight Fine-Tuning

    cs.LG 2025-05 reject novelty 4.0 of 10

    On a private 3,100-clip, 31-class drone audio dataset, full fine-tuning of EfficientNet-B0 with three augmentations reached 95.95% validation accuracy, the best of all compared models and PEFT methods.

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