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Off-road terrain classification
Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2022.
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| Other Authors: | |
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2022
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| _version_ | 1867613702615728128 |
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| access_status_str | Open Access |
| author2 | Hamersma, Herman |
| author_browse | Hamersma, Herman |
| author_facet | Hamersma, Herman |
| collection | Thesis |
| dc_rights_str_mv | © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2022. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/86675 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:40:20.984Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/86675 Off-road terrain classification Hamersma, Herman fritzlafras@gmail.com Botha, Theunis R. Fritz, Petrus Lafras Terrain classification Convolutional neural network Image data Vehicle dynamics Traffic accidents UCTD Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2022. Road traffic accidents place a burden on the global economy. This impact is reduced by the development of safer vehicles. Advanced Driver Assist Systems (ADAS) aim to reduce the frequency and severity of accidents. ADASs are designed to operate in well-defined environments, such as first world urban areas. However, 93% of fatal accidents occur in developing countries; areas often without properly maintained roads. ADAS regularly fail to perform as intended in these challenging environments. Terrain classification may improve the performance of ADAS. A lot of research has been conducted on on-road terrain classification, but few studies focus on off-road terrain classification. This study classifies several off-road terrains, based on road roughness using the ISO8608:2016 standard, using a convolutional neural network (CNN). A database of images over different terrains with known road roughness was created using forward and downward facing cameras. Two different classification models were built: one is brand new and the other made use of transfer learning on pretrained model. Terrain data was captured on several on-road and off-road tracks. Results indicate that off-road terrain classification with cameras can be done with high accuracy before a vehicle drives over a specific part of a road. Mechanical and Aeronautical Engineering MEng (Mechanical Engineering) Unrestricted 2022-08-03T09:19:26Z 2022-08-03T09:19:26Z 2022-09-07 2022 Dissertation * S2022 https://repository.up.ac.za/handle/2263/86675 en © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Terrain classification Convolutional neural network Image data Vehicle dynamics Traffic accidents UCTD Off-road terrain classification |
| title | Off-road terrain classification |
| title_full | Off-road terrain classification |
| title_fullStr | Off-road terrain classification |
| title_full_unstemmed | Off-road terrain classification |
| title_short | Off-road terrain classification |
| title_sort | off road terrain classification |
| topic | Terrain classification Convolutional neural network Image data Vehicle dynamics Traffic accidents UCTD |
| url | https://repository.up.ac.za/handle/2263/86675 |