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dc.contributor.authorGonzález-Navarro, Rubén
dc.contributor.authorLin-Yang, Da-hui
dc.contributor.authorVázquez-Martín, Ricardo 
dc.contributor.authorGarcía-Cerezo, Alfonso José 
dc.date.accessioned2023-11-29T12:57:48Z
dc.date.available2023-11-29T12:57:48Z
dc.date.created2023
dc.date.issued2023
dc.identifier.citationRubén González-Navarro, Dahui Lin-Yang, Ricardo Vázquez-Martı́n, and Alfonso Garcia-Cerezo. Disaster area recognition from aerial images with complex-shape class detection. Proc. of the IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR) 2023. pp. 126-131. Fukushima, Japan.es_ES
dc.identifier.urihttps://hdl.handle.net/10630/28176
dc.description.abstractThis paper presents a convolutional neural network (CNN) model for event detection from Unmanned Aerial Vehicles (UAV) in disaster environments. The model leverages the YOLOv5 network, specifically adapted for aerial images and optimized for detecting Search and Rescue (SAR) related classes for disaster area recognition. These SAR-related classes are person, vehicle, debris, fire, smoke, and flooded areas. Among these, the latter four classes lead to unique challenges due to their lack of discernible edges and/or shapes in aerial imagery, making their accurate detection and performance evaluation metrics particularly intricate. The methodology for the model training involves the adaptation of the pre-trained model for aerial images and its subsequent optimization for SAR scenarios. These stages have been conducted using public datasets, with the required image labeling in the case of SAR-related classes. An analysis of the obtained results demonstrates the model’s performance while discussing the intricacies related to complex-shape classes. The model and the SAR datasets are publicly available.es_ES
dc.description.sponsorshipThis work has been partially funded by the Spanish Ministerio de Ciencia, Innovación y Universidades, Gobierno de España, project PID2021- 122944OB-I00. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.es_ES
dc.language.isoenges_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectCatástrofeses_ES
dc.subjectVisión por ordenadores_ES
dc.subjectAviones sin pilotoes_ES
dc.subjectRedes neuronales (Informática)es_ES
dc.subject.otherDisaster Roboticses_ES
dc.subject.otherDeep learninges_ES
dc.subject.otherConvolutional neural networkes_ES
dc.subject.otherTransfer learninges_ES
dc.subject.otherAerial imageses_ES
dc.titleDisaster area recognition from aerial images with complex-shape class detection.es_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.centroEscuela de Ingenierías Industrialeses_ES
dc.relation.eventtitleIEEE International Symposium on Safety, Security and Rescue Robotics (SSRR) 2023es_ES
dc.relation.eventplaceFukushima, Japan.es_ES
dc.relation.eventdate11/2023es_ES
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*


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