The Implications of Weather and Reflectivity Variations on Automatic Traffic Sign Recognition Performance
Automatic recognition of traffic signs in complex, real-world environments has become a pressing research concern with rapid improvements of smart technologies. Hence, this study leveraged an industry-grade object detection and classification algorithm (You-Only-Look-Once, YOLO) to develop an automa...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/5513552 |
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| _version_ | 1832549783352377344 |
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| author | Mudasser Seraj Andres Rosales-Castellanos Amr Shalkamy Karim El-Basyouny Tony Z. Qiu |
| author_facet | Mudasser Seraj Andres Rosales-Castellanos Amr Shalkamy Karim El-Basyouny Tony Z. Qiu |
| author_sort | Mudasser Seraj |
| collection | DOAJ |
| description | Automatic recognition of traffic signs in complex, real-world environments has become a pressing research concern with rapid improvements of smart technologies. Hence, this study leveraged an industry-grade object detection and classification algorithm (You-Only-Look-Once, YOLO) to develop an automatic traffic sign recognition system that can identify widely used regulatory and warning signs in diverse driving conditions. Sign recognition performance was assessed in terms of weather and reflectivity to identify the limitations of the developed system in real-world conditions. Furthermore, we produced several editions of our sign recognition system by gradually increasing the number of training images in order to account for the significance of training resources in recognition performance. Analysis considering variable weather conditions, including fair (clear and sunny) and inclement (cloudy and snowy), demonstrated a lower susceptibility of sign recognition in the highly trained system. Analysis considering variable reflectivity conditions, including sheeting type, lighting conditions, and sign age, showed that older engineering-grade sheeting signs were more likely to go unnoticed by the developed system at night. In summary, this study incorporated automatic object detection technology to develop a novel sign recognition system to determine its real-world applicability, opportunities, and limitations for future integration with advanced driver assistance technologies. |
| format | Article |
| id | doaj-art-42fc39ab8beb49fc81c5baad0a6aab27 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-42fc39ab8beb49fc81c5baad0a6aab272025-02-03T06:08:33ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/55135525513552The Implications of Weather and Reflectivity Variations on Automatic Traffic Sign Recognition PerformanceMudasser Seraj0Andres Rosales-Castellanos1Amr Shalkamy2Karim El-Basyouny3Tony Z. Qiu4Department of Civil and Environmental Engineering, University of Alberta, Edmonton T6G 2R3, CanadaDepartment of Civil and Environmental Engineering, University of Alberta, Edmonton T6G 2R3, CanadaDepartment of Civil and Environmental Engineering, University of Alberta, Edmonton T6G 2R3, CanadaDepartment of Civil and Environmental Engineering, University of Alberta, Edmonton T6G 2R3, CanadaDepartment of Civil and Environmental Engineering, University of Alberta, Edmonton T6G 2R3, CanadaAutomatic recognition of traffic signs in complex, real-world environments has become a pressing research concern with rapid improvements of smart technologies. Hence, this study leveraged an industry-grade object detection and classification algorithm (You-Only-Look-Once, YOLO) to develop an automatic traffic sign recognition system that can identify widely used regulatory and warning signs in diverse driving conditions. Sign recognition performance was assessed in terms of weather and reflectivity to identify the limitations of the developed system in real-world conditions. Furthermore, we produced several editions of our sign recognition system by gradually increasing the number of training images in order to account for the significance of training resources in recognition performance. Analysis considering variable weather conditions, including fair (clear and sunny) and inclement (cloudy and snowy), demonstrated a lower susceptibility of sign recognition in the highly trained system. Analysis considering variable reflectivity conditions, including sheeting type, lighting conditions, and sign age, showed that older engineering-grade sheeting signs were more likely to go unnoticed by the developed system at night. In summary, this study incorporated automatic object detection technology to develop a novel sign recognition system to determine its real-world applicability, opportunities, and limitations for future integration with advanced driver assistance technologies.http://dx.doi.org/10.1155/2021/5513552 |
| spellingShingle | Mudasser Seraj Andres Rosales-Castellanos Amr Shalkamy Karim El-Basyouny Tony Z. Qiu The Implications of Weather and Reflectivity Variations on Automatic Traffic Sign Recognition Performance Journal of Advanced Transportation |
| title | The Implications of Weather and Reflectivity Variations on Automatic Traffic Sign Recognition Performance |
| title_full | The Implications of Weather and Reflectivity Variations on Automatic Traffic Sign Recognition Performance |
| title_fullStr | The Implications of Weather and Reflectivity Variations on Automatic Traffic Sign Recognition Performance |
| title_full_unstemmed | The Implications of Weather and Reflectivity Variations on Automatic Traffic Sign Recognition Performance |
| title_short | The Implications of Weather and Reflectivity Variations on Automatic Traffic Sign Recognition Performance |
| title_sort | implications of weather and reflectivity variations on automatic traffic sign recognition performance |
| url | http://dx.doi.org/10.1155/2021/5513552 |
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