Traffic sign classification using transfer learning: An investigation of feature-combining model
The traffic sign classification system is a technology to help drivers to recognise the traffic sign hence reducing the accident. Many types of learning models have been applied to this technology recently. However, the deployment of learning models is unknown and shown to be non-trivial towards ima...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Penerbit UMP
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/33979/ http://umpir.ump.edu.my/id/eprint/33979/1/Traffic%20sign%20classification%20using%20transfer%20learning.pdf |
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| author | Lim, Wee Sheng Ahmad Fakhri, Ab. Nasir Mohd Azraai, Mohd Razman Anwar P. P., Abdul Majeed Nur Shazwani, Kamarudin Muhammad Zulfahmi Toh, Abdullah |
| author_facet | Lim, Wee Sheng Ahmad Fakhri, Ab. Nasir Mohd Azraai, Mohd Razman Anwar P. P., Abdul Majeed Nur Shazwani, Kamarudin Muhammad Zulfahmi Toh, Abdullah |
| author_sort | Lim, Wee Sheng |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The traffic sign classification system is a technology to help drivers to recognise the traffic sign hence reducing the accident. Many types of learning models have been applied to this technology recently. However, the deployment of learning models is unknown and shown to be non-trivial towards image classification and object detection. The implementation of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features as well as can save lots of training time. Besides, the feature-combining model exhibited great performance in the TL method in many applications. Nonetheless, the utilisation of such methods towards traffic sign classification applications are not yet being evaluated. The present study aims to exploit and investigate the effectiveness of transfer learning feature-combining models, particularly to classify traffic signs. The images were gathered from GTSRB dataset which consists of 10 different types of traffic signs i.e. warning, stop, repair, not enter, traffic light, turn right, speed limit (80km/s), speed limit (50km/s), speed limit (60km/s), and turn left sign board. A total of 7000 images were then split to 70:30 for train and test ratio using a stratified method. The VGG16 and VGG19 TL-features models were used to combine with two classifiers, Random Forest (RF) and Neural Network. In summary, six different pipelines were trained and tested. From the results obtained, the best pipeline was VGG16+VGG19 with RF classifier, which was able to yield an average classification accuracy of 0.9838. The findings showed that the feature-combining model successfully classifies the traffic signs much better than the single TL-feature model. The investigation would be useful for traffic signs classification applications i.e. for ADAS systems |
| first_indexed | 2025-11-15T03:12:18Z |
| format | Article |
| id | ump-33979 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:12:18Z |
| publishDate | 2021 |
| publisher | Penerbit UMP |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-339792022-05-09T04:02:33Z http://umpir.ump.edu.my/id/eprint/33979/ Traffic sign classification using transfer learning: An investigation of feature-combining model Lim, Wee Sheng Ahmad Fakhri, Ab. Nasir Mohd Azraai, Mohd Razman Anwar P. P., Abdul Majeed Nur Shazwani, Kamarudin Muhammad Zulfahmi Toh, Abdullah QA76 Computer software T Technology (General) TK Electrical engineering. Electronics Nuclear engineering The traffic sign classification system is a technology to help drivers to recognise the traffic sign hence reducing the accident. Many types of learning models have been applied to this technology recently. However, the deployment of learning models is unknown and shown to be non-trivial towards image classification and object detection. The implementation of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features as well as can save lots of training time. Besides, the feature-combining model exhibited great performance in the TL method in many applications. Nonetheless, the utilisation of such methods towards traffic sign classification applications are not yet being evaluated. The present study aims to exploit and investigate the effectiveness of transfer learning feature-combining models, particularly to classify traffic signs. The images were gathered from GTSRB dataset which consists of 10 different types of traffic signs i.e. warning, stop, repair, not enter, traffic light, turn right, speed limit (80km/s), speed limit (50km/s), speed limit (60km/s), and turn left sign board. A total of 7000 images were then split to 70:30 for train and test ratio using a stratified method. The VGG16 and VGG19 TL-features models were used to combine with two classifiers, Random Forest (RF) and Neural Network. In summary, six different pipelines were trained and tested. From the results obtained, the best pipeline was VGG16+VGG19 with RF classifier, which was able to yield an average classification accuracy of 0.9838. The findings showed that the feature-combining model successfully classifies the traffic signs much better than the single TL-feature model. The investigation would be useful for traffic signs classification applications i.e. for ADAS systems Penerbit UMP 2021 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/33979/1/Traffic%20sign%20classification%20using%20transfer%20learning.pdf Lim, Wee Sheng and Ahmad Fakhri, Ab. Nasir and Mohd Azraai, Mohd Razman and Anwar P. P., Abdul Majeed and Nur Shazwani, Kamarudin and Muhammad Zulfahmi Toh, Abdullah (2021) Traffic sign classification using transfer learning: An investigation of feature-combining model. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 3 (2). pp. 37-41. ISSN 2637-0883. (Published) https://doi.org/10.15282/mekatronika.v3i2.7346 https://doi.org/10.15282/mekatronika.v3i2.7346 |
| spellingShingle | QA76 Computer software T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Lim, Wee Sheng Ahmad Fakhri, Ab. Nasir Mohd Azraai, Mohd Razman Anwar P. P., Abdul Majeed Nur Shazwani, Kamarudin Muhammad Zulfahmi Toh, Abdullah Traffic sign classification using transfer learning: An investigation of feature-combining model |
| title | Traffic sign classification using transfer learning: An investigation of feature-combining model |
| title_full | Traffic sign classification using transfer learning: An investigation of feature-combining model |
| title_fullStr | Traffic sign classification using transfer learning: An investigation of feature-combining model |
| title_full_unstemmed | Traffic sign classification using transfer learning: An investigation of feature-combining model |
| title_short | Traffic sign classification using transfer learning: An investigation of feature-combining model |
| title_sort | traffic sign classification using transfer learning: an investigation of feature-combining model |
| topic | QA76 Computer software T Technology (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/33979/ http://umpir.ump.edu.my/id/eprint/33979/ http://umpir.ump.edu.my/id/eprint/33979/ http://umpir.ump.edu.my/id/eprint/33979/1/Traffic%20sign%20classification%20using%20transfer%20learning.pdf |