Abstract
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.
Highlights
Traffic signs are used to regulate, warn, and guide traffic on roadways and facilitate coordinated road usage [1], and their placement, orientation, and visibility are crucial for road operation and safety
Our study emphasised on detection sensitivity to TSR system training resources which restrained us from analyzing asymmetrical impact due to training resources with varying lighting and environmental features
Research is study has contributed to the development of a new, robust automatic TSR system through the unique integration of industry-ready technology with an experimental investigation of the TSR system’s limitations and opportunities. e primary objective of the research was to study the influence of weather and reflectivity variations on the TSR system’s performance, as the system was improved consistently through the gradual increase in the number of training images. e results from the recognition performance analysis found that the impact of reflectivity conditions was far more significant than that of weather variations
Summary
Traffic signs are used to regulate, warn, and guide traffic on roadways and facilitate coordinated road usage [1], and their placement, orientation, and visibility are crucial for road operation and safety. Given their importance, automatic recognition of roadway signs by smart transportation technology is a current research interest with numerous potential applications [2]. E scope of this research is limited to identifying the changes of performance pattern due to Journal of Advanced Transportation variations in real-world weather and reflectivity conditions resulting from an industry-level object detection system. The TSR system was only trained with images from favourable environmental condition which implies the proficiency of the developed system in identifying the signs in unfavourable condition without being trained for such conditions
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