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Open AccessArticle

Audio-Based Aircraft Detection System for Safe RPAS BVLOS Operations

1
Avionics and Systems, Advanced Center for Aerospace Technologies (FADA-CATEC), 41309 Sevilla, Spain
2
Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, London SE1 7EH, UK
3
RPAS, Aertec Solutions, 41309 Sevilla, Spain
4
Robotics, Vision and Control Group (GRVC), University of Seville, 41092 Seville, Spain
*
Authors to whom correspondence should be addressed.
Electronics 2020, 9(12), 2076; https://doi.org/10.3390/electronics9122076
Received: 15 October 2020 / Revised: 1 December 2020 / Accepted: 3 December 2020 / Published: 5 December 2020
(This article belongs to the Special Issue Deep Learning Technologies for Machine Vision and Audition)
For the Remotely Piloted Aircraft Systems (RPAS) market to continue its current growth rate, cost-effective ‘Detect and Avoid’ systems that enable safe beyond visual line of sight (BVLOS) operations are critical. We propose an audio-based ‘Detect and Avoid’ system, composed of microphones and an embedded computer, which performs real-time inferences using a sound event detection (SED) deep learning model. Two state-of-the-art SED models, YAMNet and VGGish, are fine-tuned using our dataset of aircraft sounds and their performances are compared for a wide range of configurations. YAMNet, whose MobileNet architecture is designed for embedded applications, outperformed VGGish both in terms of aircraft detection and computational performance. YAMNet’s optimal configuration, with >70% true positive rate and precision, results from combining data augmentation and undersampling with the highest available inference frequency (i.e., 10 Hz). While our proposed ‘Detect and Avoid’ system already allows the detection of small aircraft from sound in real time, additional testing using multiple aircraft types is required. Finally, a larger training dataset, sensor fusion, or remote computations on cloud-based services could further improve system performance. View Full-Text
Keywords: deep learning; sound event detection; convolutional neural networks; audio processing; embedded systems deep learning; sound event detection; convolutional neural networks; audio processing; embedded systems
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MDPI and ACS Style

Mariscal-Harana, J.; Alarcón, V.; González, F.; Calvente, J.J.; Pérez-Grau, F.J.; Viguria, A.; Ollero, A. Audio-Based Aircraft Detection System for Safe RPAS BVLOS Operations. Electronics 2020, 9, 2076. https://doi.org/10.3390/electronics9122076

AMA Style

Mariscal-Harana J, Alarcón V, González F, Calvente JJ, Pérez-Grau FJ, Viguria A, Ollero A. Audio-Based Aircraft Detection System for Safe RPAS BVLOS Operations. Electronics. 2020; 9(12):2076. https://doi.org/10.3390/electronics9122076

Chicago/Turabian Style

Mariscal-Harana, Jorge; Alarcón, Víctor; González, Fidel; Calvente, Juan J.; Pérez-Grau, Francisco J.; Viguria, Antidio; Ollero, Aníbal. 2020. "Audio-Based Aircraft Detection System for Safe RPAS BVLOS Operations" Electronics 9, no. 12: 2076. https://doi.org/10.3390/electronics9122076

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