Object Classification using 3D Convolutional Neural Networks

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dc.contributor.authorBender, Axel
dc.contributor.authorÞorsteinsson Marel, Elías
dc.contributor.departmentChalmers tekniska högskola / Institutionen för energi och miljösv
dc.contributor.departmentChalmers University of Technology / Department of Energy and Environmenten
dc.date.accessioned2019-07-03T14:27:37Z
dc.date.available2019-07-03T14:27:37Z
dc.date.issued2016
dc.description.abstractIn the development of autonomous driving and active safety systems, knowledge about the vehicle’s surroundings is critical. When it comes to making decisions in real driving scenarios, the location and relative movement of surrounding vehicles, pedestrians and even static objects gives invaluable information to the system responsible for decision making. To know whether an object is a car or pedestrian, the system has to distinguish between the different features to predict object type. LiDAR sensors are among the most commonly used sensors in the development of modern AD systems as they produce dense images of their surroundings that are relatively resistant to changing light and weather conditions. Many classification methods use feature extraction or transformations to evaluate the 3D information using methods commonly used in 2D image analysis. In this thesis we evaluate the performance of training convolutional neural networks directly on 3D data, bypassing any information loss through data extraction or transformation and allowing the intensity hit of points to be used. The effectiveness of the method is evaluated on a dataset created from the KITTI Vision Benchmarking Suite. Our results show a total accuracy score of 96.35% and with a mean accuracy of 95.67% on a dataset trained on 7 classes.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/249371
dc.language.isoeng
dc.relation.ispartofseriesRapportserie för Avdelningen för fysisk resursteori : 2016:11
dc.setspec.uppsokLifeEarthScience
dc.subjectBuilding Futures
dc.subjectEnergi
dc.subjectInformations- och kommunikationsteknik
dc.subjectTransport
dc.subjectHållbar utveckling
dc.subjectData- och systemvetenskap
dc.subjectDatorteknik
dc.subjectInfrastrukturteknik
dc.subjectRobotteknik och automation
dc.subjectBuilding Futures
dc.subjectEnergy
dc.subjectInformation & Communication Technology
dc.subjectTransport
dc.subjectSustainable Development
dc.subjectComputer and systems science
dc.subjectComputer Engineering
dc.subjectInfrastructure Engineering
dc.subjectRobotics
dc.titleObject Classification using 3D Convolutional Neural Networks
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
local.programmeSystems, control and mechatronics (MPSYS), MSc
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