Deep learning methods for recognizing signs/objects in road traffic
dc.contributor.author | Bengtson, Josef | |
dc.contributor.author | Heikkilä, Filip | |
dc.contributor.author | Nilsson, Per | |
dc.contributor.author | Nyström, Lukas | |
dc.contributor.author | Persson, Erik | |
dc.contributor.author | Tellwe, Gustav | |
dc.contributor.department | Chalmers tekniska högskola / Institutionen för elektroteknik | sv |
dc.contributor.department | Chalmers University of Technology / Department of Electrical Engineering | en |
dc.date.accessioned | 2019-07-03T14:52:16Z | |
dc.date.available | 2019-07-03T14:52:16Z | |
dc.date.issued | 2018 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12380/255863 | |
dc.language.iso | eng | |
dc.relation.ispartofseries | Kandidatarbete - Institutionen för elektroteknik, Chalmers tekniska högskola | |
dc.setspec.uppsok | Technology | |
dc.subject | Elektroteknik och elektronik | |
dc.subject | Electrical Engineering, Electronic Engineering, Information Engineering | |
dc.title | Deep learning methods for recognizing signs/objects in road traffic | |
dc.type.degree | Examensarbete för kandidatexamen | sv |
dc.type.degree | Bachelor Thesis | en |
dc.type.uppsok | M2 | |
local.programme | Engineering Mathematics (300 hp) |
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