Abstract
This paper aims to apply a YOLOv3 technique for detecting and recognizing Thai traffic signs in real-time environments. The Thai Traffic Sign Dataset (TTSD) was collected by car cameras to store the video images using the resolution of 1920 × 1080 pixels using 60 frames per second, and a 1280 × 720 pixels and 30 frames per second. In addition, the data was collected in the rural area of Maha Sarakham Province and Kalasin Province. The dataset was generated and distributed for general traffic sign detection and recognition. Two architectures (YOLOv3 and YOLOv3 Tiny) are compared with 50 classes of road signs and 200 badges in each class, containing 9,357 images. The experiment shows that the mean average precision (mAP) of YOLOv3 (88.10%) is better than YOLOv3 Tiny (80.84%) while the speed of YOLOv3 marginally is better than YOLOv3.
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Acknowledgement
We gratefully acknowledge Asst. Prof. Dr. Thawatchai Chomsiri for supporting the PC and GPU for the experiment.
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Thipsanthia, P., Chamchong, R., Songram, P. (2019). Road Sign Detection and Recognition of Thai Traffic Based on YOLOv3. In: Chamchong, R., Wong, K. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2019. Lecture Notes in Computer Science(), vol 11909. Springer, Cham. https://doi.org/10.1007/978-3-030-33709-4_25
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DOI: https://doi.org/10.1007/978-3-030-33709-4_25
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