dc.creator | Mariscal Harana, Jorge | es |
dc.creator | Alarcón, Víctor | es |
dc.creator | González, Fidel | es |
dc.creator | Calvente, Juan José | es |
dc.creator | Pérez Grau, Francisco Javier | es |
dc.creator | Viguria, Antidio Jiménez | es |
dc.creator | Ollero Baturone, Aníbal | es |
dc.date.accessioned | 2022-02-08T14:33:30Z | |
dc.date.available | 2022-02-08T14:33:30Z | |
dc.date.issued | 2020-12 | |
dc.identifier.citation | Mariscal Harana, J., Alarcón, V., González, F., Calvente, J.J., Pérez Grau, F.J., Viguria, A.J. y Ollero Baturone, A. (2020). Audio-Based Aircraft Detection System for Safe RPAS BVLOS Operations. Electronics, 9 (12). Article number 2076. | |
dc.identifier.issn | EISSN 2079-9292 | es |
dc.identifier.uri | https://hdl.handle.net/11441/129777 | |
dc.description.abstract | 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. | es |
dc.description.sponsorship | Centro para el Desarrollo Tecnológico Industrial-VIGIA (ITC-20181032) | es |
dc.description.sponsorship | Centro para el Desarrollo Tecnológico Industrial-iMOV3D (CER-20191007) | es |
dc.format | application/pdf | es |
dc.format.extent | 13 p. | es |
dc.language.iso | eng | es |
dc.publisher | MDPI | es |
dc.relation.ispartof | Electronics, 9 (12). Article number 2076. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Deep learning | es |
dc.subject | Sound event detection | es |
dc.subject | Convolutional neural networks | es |
dc.subject | Audio processing | es |
dc.subject | Embedded systems | es |
dc.title | Audio-Based Aircraft Detection System for Safe RPAS BVLOS Operations | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática | es |
dc.relation.projectID | ITC-20181032 | es |
dc.relation.projectID | CER-20191007 | es |
dc.relation.publisherversion | http://dx.doi.org/ 10.3390/electronics9122076 | es |
dc.identifier.doi | 10.3390/electronics9122076 | es |
dc.contributor.group | Universidad de Sevilla. TEP151: Robótica, Visión y Control | es |
dc.journaltitle | Electronics | es |
dc.publication.volumen | 9 | es |
dc.publication.issue | 12 | es |
dc.publication.initialPage | Article number 2076 | es |
dc.contributor.funder | European Commission (EC). Fondo Europeo de Desarrollo Regional (FEDER) | es |
dc.contributor.funder | Ministerio de Ciencia, Innovación y Universidades (MICINN). España | es |