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dc.contributor.authorLeira, Frederik Stendahl
dc.contributor.authorJohansen, Tor Arne
dc.contributor.authorFossen, Thor I.
dc.date.accessioned2018-04-30T09:04:26Z
dc.date.available2018-04-30T09:04:26Z
dc.date.created2015-05-19T01:22:24Z
dc.date.issued2015
dc.identifier.citationIEEE Aerospace Conference. Proceedings. 2015, 2015-June .nb_NO
dc.identifier.issn1095-323X
dc.identifier.urihttp://hdl.handle.net/11250/2496489
dc.description.abstractThe use of unmanned aerial vehicles (UAVs) that can operate autonomously in dynamic and dangerous operational environments are becoming increasingly common. In such operations, object detection, classification and tracking can often be one of the main goals. In recent years there has been an increased focus on embedded hardware that is both small and powerful, making UAV on-board data processing more viable. Being able to process the video feed on-board the UAV calls for fast and robust real-time algorithms for object identification and tracking. This paper discusses the development and implementation of a machine vision system for a low-cost fixed-wing UAV with a total flying weight of less than 4kg. The machine vision system incorporates the use of a thermal imaging camera and on-board processing power to perform real-time object detection, classification and tracking of objects in the ocean surface. The system is tested on thermal video data from a test flight, and is found to be able to detect 99;6% of objects of interest located in the ocean surface. Of the detected objects, only 5% were false positives. Furthermore, it classifies 93; 3% of the object types it is trained to classify correctly. The classifier is highly agile, allowing the user to quickly define which object characteristics that should be considered during classification, and what types of objects to classify. Finally, the system is found to successfully track 85% of the object types it is actively searching for in a real-time simulation test.nb_NO
dc.language.isoengnb_NO
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)nb_NO
dc.titleAutomatic detection, classification and tracking of objects in the ocean surface from UAVs using a thermal cameranb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber10nb_NO
dc.source.volume2015-Junenb_NO
dc.source.journalIEEE Aerospace Conference. Proceedingsnb_NO
dc.identifier.doi10.1109/AERO.2015.7119238
dc.identifier.cristin1243171
dc.relation.projectNorges forskningsråd: 195143nb_NO
dc.relation.projectNorges forskningsråd: 223254nb_NO
dc.relation.projectNorges forskningsråd: 235348nb_NO
dc.description.localcode© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.nb_NO
cristin.unitcode194,63,25,0
cristin.unitnameInstitutt for teknisk kybernetikk
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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