Malaysia traffic sign recognition with convolutional neural network
Traffic sign recognition system is an important subsystem in advanced driver assistance systems (ADAS) that assisting a driver to detect a critical driving scenario and subsequently making an immediate decision. Recently, deep architecture neural network is popular because it adapts well in various...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/46509 |
| _version_ | 1848757578197106688 |
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| author | Lau, M. Lim, Hann Gopalai, A. |
| author_facet | Lau, M. Lim, Hann Gopalai, A. |
| author_sort | Lau, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Traffic sign recognition system is an important subsystem in advanced driver assistance systems (ADAS) that assisting a driver to detect a critical driving scenario and subsequently making an immediate decision. Recently, deep architecture neural network is popular because it adapts well in various kind of scenarios, even those which were not used during training. Therefore, a deep architecture neural network is implemented to perform traffic sign classification in order to improve the traffic sign recognition rate. A comparative study for a deep and shallow architecture neural network is presented in this paper. Deep and shallow architecture neural network refer to convolutional neural network (CNN) and radial basis function neural network (RBFNN) respectively. In the simulation result, two types of training modes had been compared i.e. incremental training and batch training. Experimental results show that incremental training mode trains faster than batch training mode. The performance of the convolutional neural network is evaluated with the Malaysian traffic sign database and achieves 99% of the recognition rate. |
| first_indexed | 2025-11-14T09:30:19Z |
| format | Conference Paper |
| id | curtin-20.500.11937-46509 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:30:19Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-465092017-09-13T13:39:37Z Malaysia traffic sign recognition with convolutional neural network Lau, M. Lim, Hann Gopalai, A. Traffic sign recognition system is an important subsystem in advanced driver assistance systems (ADAS) that assisting a driver to detect a critical driving scenario and subsequently making an immediate decision. Recently, deep architecture neural network is popular because it adapts well in various kind of scenarios, even those which were not used during training. Therefore, a deep architecture neural network is implemented to perform traffic sign classification in order to improve the traffic sign recognition rate. A comparative study for a deep and shallow architecture neural network is presented in this paper. Deep and shallow architecture neural network refer to convolutional neural network (CNN) and radial basis function neural network (RBFNN) respectively. In the simulation result, two types of training modes had been compared i.e. incremental training and batch training. Experimental results show that incremental training mode trains faster than batch training mode. The performance of the convolutional neural network is evaluated with the Malaysian traffic sign database and achieves 99% of the recognition rate. 2015 Conference Paper http://hdl.handle.net/20.500.11937/46509 10.1109/ICDSP.2015.7252029 restricted |
| spellingShingle | Lau, M. Lim, Hann Gopalai, A. Malaysia traffic sign recognition with convolutional neural network |
| title | Malaysia traffic sign recognition with convolutional neural network |
| title_full | Malaysia traffic sign recognition with convolutional neural network |
| title_fullStr | Malaysia traffic sign recognition with convolutional neural network |
| title_full_unstemmed | Malaysia traffic sign recognition with convolutional neural network |
| title_short | Malaysia traffic sign recognition with convolutional neural network |
| title_sort | malaysia traffic sign recognition with convolutional neural network |
| url | http://hdl.handle.net/20.500.11937/46509 |