How can sustainable public transport be improved? A traffic sign recognition approach using convolutional neural network
Sustainable public transport is an important factor to boost urban economic development, and it is also an important part of building a low-carbon environmental society. The application of driverless technology in public transport injects new impetus into its sustainable development. Road traffic si...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Published: |
MDPI AG
2022
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| Online Access: | http://hdl.handle.net/20.500.11937/89418 |
| _version_ | 1848765215250841600 |
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| author | Liu, Jingjing Ge, Hongwei Li, Jiajie He, Pengcheng Hao, Zhangang Hitch, Michael |
| author_facet | Liu, Jingjing Ge, Hongwei Li, Jiajie He, Pengcheng Hao, Zhangang Hitch, Michael |
| author_sort | Liu, Jingjing |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Sustainable public transport is an important factor to boost urban economic development, and it is also an important part of building a low-carbon environmental society. The application of driverless technology in public transport injects new impetus into its sustainable development. Road traffic sign recognition is the key technology of driverless public transport. It is particularly important to adopt innovative algorithms to optimize the accuracy of traffic sign recognition and build sustainable public transport. Therefore, this paper proposes a convolutional neural network (CNN) based on k-means to optimize the accuracy of traffic sign recognition, and it proposes a sparse maximum CNN to identify difficult traffic signs through hierarchical classification. In the rough classification stage, k-means CNN is used to extract features, and improved support vector machine (SVM) is used for classification. Then, in the fine classification stage, sparse maximum CNN is used for classification. The research results show that the algorithm improves the accuracy of traffic sign recognition more comprehensively and effectively, and it can be effectively applied in unmanned driving technology, which will also bring new breakthroughs for the sustainable development of public transport. |
| first_indexed | 2025-11-14T11:31:42Z |
| format | Journal Article |
| id | curtin-20.500.11937-89418 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:31:42Z |
| publishDate | 2022 |
| publisher | MDPI AG |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-894182022-10-24T06:59:49Z How can sustainable public transport be improved? A traffic sign recognition approach using convolutional neural network Liu, Jingjing Ge, Hongwei Li, Jiajie He, Pengcheng Hao, Zhangang Hitch, Michael Sustainable public transport is an important factor to boost urban economic development, and it is also an important part of building a low-carbon environmental society. The application of driverless technology in public transport injects new impetus into its sustainable development. Road traffic sign recognition is the key technology of driverless public transport. It is particularly important to adopt innovative algorithms to optimize the accuracy of traffic sign recognition and build sustainable public transport. Therefore, this paper proposes a convolutional neural network (CNN) based on k-means to optimize the accuracy of traffic sign recognition, and it proposes a sparse maximum CNN to identify difficult traffic signs through hierarchical classification. In the rough classification stage, k-means CNN is used to extract features, and improved support vector machine (SVM) is used for classification. Then, in the fine classification stage, sparse maximum CNN is used for classification. The research results show that the algorithm improves the accuracy of traffic sign recognition more comprehensively and effectively, and it can be effectively applied in unmanned driving technology, which will also bring new breakthroughs for the sustainable development of public transport. 2022 Journal Article http://hdl.handle.net/20.500.11937/89418 http://creativecommons.org/licenses/by/4.0/ MDPI AG fulltext |
| spellingShingle | Liu, Jingjing Ge, Hongwei Li, Jiajie He, Pengcheng Hao, Zhangang Hitch, Michael How can sustainable public transport be improved? A traffic sign recognition approach using convolutional neural network |
| title | How can sustainable public transport be improved? A traffic sign recognition approach using convolutional neural network |
| title_full | How can sustainable public transport be improved? A traffic sign recognition approach using convolutional neural network |
| title_fullStr | How can sustainable public transport be improved? A traffic sign recognition approach using convolutional neural network |
| title_full_unstemmed | How can sustainable public transport be improved? A traffic sign recognition approach using convolutional neural network |
| title_short | How can sustainable public transport be improved? A traffic sign recognition approach using convolutional neural network |
| title_sort | how can sustainable public transport be improved? a traffic sign recognition approach using convolutional neural network |
| url | http://hdl.handle.net/20.500.11937/89418 |