Evaluation Of The Neural Network Object Detection In Multi-Modal Images

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but.event.date27.04.2021cs
but.event.titleSTUDENT EEICT 2021cs
dc.contributor.authorLigocki, Adam
dc.date.accessioned2023-01-06T10:05:43Z
dc.date.available2023-01-06T10:05:43Z
dc.date.issued2021cs
dc.description.abstractThis paper studies the information gain of various data domains that are commonly usedin the modern Advanced Driving Assistant Systems (ADAS) to develop robust systems that wouldincrease traffic safety. We could see a fast growth of many Deep Convolutional Neural Networks(DCNN) based solutions during the last several years. These methods are state-of-the-art in objectdetection and semantic scene segmentation. We created a small annotated dataset of synchronizedRGB, grayscale, thermal, and depth map images and used the modern DCNN framework tool toevaluate the object detection robustness of different data domains and their information gain processunderstanding the surrounding environment of the semi-autonomous driving agent.en
dc.formattextcs
dc.format.extent156-160cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings II of the 27st Conference STUDENT EEICT 2021: Selected Papers. s. 156-160. ISBN 978-80-214-5943-4cs
dc.identifier.doi10.13164/eeict.2021.156
dc.identifier.isbn978-80-214-5943-4
dc.identifier.urihttp://hdl.handle.net/11012/200832
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings II of the 27st Conference STUDENT EEICT 2021: Selected papersen
dc.relation.urihttps://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazenics
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectMulti-modalen
dc.subjectObject Detectionen
dc.subjectConvolutional Neural Networken
dc.subjectRGBen
dc.subjectGrayscaleen
dc.subjectThermal,IRen
dc.subjectDepth Mapen
dc.titleEvaluation Of The Neural Network Object Detection In Multi-Modal Imagesen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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