Vietnamese traffic sign recognition using deep learning
Published in Proceedings of the 2024 9th International Conference on Intelligent Information Technology, 2024
Abstract. Recognizing the importance of road safety, traffic signs are designed in a variety of shapes, colors, and images to guide and alert drivers. Overlooking these signs, particularly the crucial ones, due to various factors, can lead to legal violations or even catastrophic accidents, impacting multiple aspects of our lives. Consequently, the use of computer systems for traffic sign recognition is urgently needed for the safety of commuters. This not only plays a pivotal role in improving our continually evolving technology but also has substantial value in academic research. Due to the complexity of terrain and the influence of weather on outcomes, Traffic Sign Recognition (TSR) remains a high-priority challenge. This article focuses on the creation of a Vietnamese traffic sign dataset comprising 46 classes. Subsequently, we implement various detection and recognition models using this dataset to assess their performance.
Recommended citation: Dinh Thuan Nguyen, Minh Khanh Phan, Phuong-Nam Tran, Duc Ngoc Minh Dang. (2024). "Vietnamese traffic sign recognition using deep learning." Proceedings of the 2024 9th International Conference on Intelligent Information Technology, 30-35.
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