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dc.contributor.authorGarcía, Jesús M.
dc.contributor.authorMartínez-Rodríguez, Jorge Luis 
dc.contributor.authorReina-Terol, Antonio Jesús 
dc.date.accessioned2024-02-01T11:38:54Z
dc.date.available2024-02-01T11:38:54Z
dc.date.issued2023-01-11
dc.identifier.citationJ. M. García, J. L. Martínez and A. J. Reina, "Bridge Crane Monitoring using a 3D LiDAR and Deep Learning," in IEEE Latin America Transactions, vol. 21, no. 2, pp. 207-216, Feb. 2023, doi: 10.1109/TLA.2023.10015213es_ES
dc.identifier.urihttps://hdl.handle.net/10630/29601
dc.descriptionPOLÍTICA DE ACCESO ABIERTO TOMADA DE: https://v2.sherpa.ac.uk/romeo/search.htmles_ES
dc.description.abstractThe use of overhead cranes in warehouses and factories has advantages for handling and transporting bulky and/or heavy loads. But it also involves risks such as collisions with other fixed or mobile elements in the working environment. Different types of sensors have been used for monitoring its operation, mainly artificial vision. In this paper, it is employed a three-dimensional (3D) LiDAR to capture the workspace of a bridge crane. The point clouds generated by this laser sensor are delivered to a convolutional neural network to detect the position of the bridge and its carriage, which allows to locate the hook and the suspended load afterwards. Additionally, the laser scans can also be used to warn the operator of possible collisions with fixed elements of the warehouse. The tests carried out show that the proposed system can be successfully used for monitoring overhead cranes.es_ES
dc.description.sponsorshipFundación Carolina de Españaes_ES
dc.language.isoenges_ES
dc.publisherIEEEes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectGrúas - Control automáticoes_ES
dc.subjectInteligencia artificiales_ES
dc.subjectRedes neuronales (Informática)es_ES
dc.subjectDispositivos de seguridades_ES
dc.subject.otherBridge cranees_ES
dc.subject.otherCollision detectiones_ES
dc.subject.otherConvolutional neural networkes_ES
dc.subject.otherDeep learninges_ES
dc.subject.other3D LiDARes_ES
dc.titleBridge Crane Monitoring using a 3D LiDAR and Deep Learning.es_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroEscuela de Ingenierías Industrialeses_ES
dc.identifier.doi10.1109/TLA.2023.10015213
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersiones_ES


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