Visual monitoring of landing gear in fighters using deep learning

The analysis of images using deep learning techniques makes it possible to detect anomalous or dangerous situations in different fields of application. This work aims to ensure the correct configuration of landing gear during aircraft landings. In contrast with other works, the small object detectio...

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Autores: Latre Campo, Jesús, Bueno Crespo, Andrés, Rodríguez Bermúdez, Germán, Pereñíguez García, Fernando
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad Católica San Antonio de Murcia (UCAM)
Repositorio:RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
OAI Identifier:oai:repositorio.ucam.edu:10952/10522
Acceso en línea:http://hdl.handle.net/10952/10522
Access Level:acceso abierto
Palabra clave:Deep learning
Convolutional neural network (CNN)
Image classification
Landing gear detection
Artificial intelligence air forces
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spelling Visual monitoring of landing gear in fighters using deep learningLatre Campo, JesúsBueno Crespo, AndrésRodríguez Bermúdez, GermánPereñíguez García, FernandoDeep learningConvolutional neural network (CNN)Image classificationLanding gear detectionArtificial intelligence air forcesThe analysis of images using deep learning techniques makes it possible to detect anomalous or dangerous situations in different fields of application. This work aims to ensure the correct configuration of landing gear during aircraft landings. In contrast with other works, the small object detection problem is solved using background subtraction technique, and subsequently feeding it to our proposed convolutional neural network to automatically classify the position of the landing gear. This work also develops a new database that combines synthetic and real images, generated from exclusive fighter landing manoeuvres performed by a real test pilot. The obtained model, trained with synthetic data and tested with real images, presents a 0.9981 of accuracy. The result is a functional system, tested against real images and endowed with ‘‘early warning’’ capability as it is able to detect the position of an aircraft’s landing gear in advance and prevent catastrophic accidents.Ingeniería, Industria y ConstrucciónEscuela Politécnica2025info:eu-repo/semantics/articlehttp://hdl.handle.net/10952/10522reponame:RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murciainstname:Universidad Católica San Antonio de Murcia (UCAM)Inglésinfo:eu-repo/semantics/openAccessoai:repositorio.ucam.edu:10952/105222026-06-07T18:35:21Z
dc.title.none.fl_str_mv Visual monitoring of landing gear in fighters using deep learning
title Visual monitoring of landing gear in fighters using deep learning
spellingShingle Visual monitoring of landing gear in fighters using deep learning
Latre Campo, Jesús
Deep learning
Convolutional neural network (CNN)
Image classification
Landing gear detection
Artificial intelligence air forces
title_short Visual monitoring of landing gear in fighters using deep learning
title_full Visual monitoring of landing gear in fighters using deep learning
title_fullStr Visual monitoring of landing gear in fighters using deep learning
title_full_unstemmed Visual monitoring of landing gear in fighters using deep learning
title_sort Visual monitoring of landing gear in fighters using deep learning
dc.creator.none.fl_str_mv Latre Campo, Jesús
Bueno Crespo, Andrés
Rodríguez Bermúdez, Germán
Pereñíguez García, Fernando
author Latre Campo, Jesús
author_facet Latre Campo, Jesús
Bueno Crespo, Andrés
Rodríguez Bermúdez, Germán
Pereñíguez García, Fernando
author_role author
author2 Bueno Crespo, Andrés
Rodríguez Bermúdez, Germán
Pereñíguez García, Fernando
author2_role author
author
author
dc.subject.none.fl_str_mv Deep learning
Convolutional neural network (CNN)
Image classification
Landing gear detection
Artificial intelligence air forces
topic Deep learning
Convolutional neural network (CNN)
Image classification
Landing gear detection
Artificial intelligence air forces
description The analysis of images using deep learning techniques makes it possible to detect anomalous or dangerous situations in different fields of application. This work aims to ensure the correct configuration of landing gear during aircraft landings. In contrast with other works, the small object detection problem is solved using background subtraction technique, and subsequently feeding it to our proposed convolutional neural network to automatically classify the position of the landing gear. This work also develops a new database that combines synthetic and real images, generated from exclusive fighter landing manoeuvres performed by a real test pilot. The obtained model, trained with synthetic data and tested with real images, presents a 0.9981 of accuracy. The result is a functional system, tested against real images and endowed with ‘‘early warning’’ capability as it is able to detect the position of an aircraft’s landing gear in advance and prevent catastrophic accidents.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10952/10522
url http://hdl.handle.net/10952/10522
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
instname:Universidad Católica San Antonio de Murcia (UCAM)
instname_str Universidad Católica San Antonio de Murcia (UCAM)
reponame_str RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
collection RIUCAM. Repositorio Institucional de la Universidad Católica San Antonio de Murcia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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