Towards real-time vietnamese traffic sign recognition on embedded systems
Published in 2024 15th International Conference on Information and Communication Technology Convergence (ICTC), 2024
Abstract (excerpt). AI development has brought many significant changes in various aspects of our daily lives in recent years. Integrating AI technology into various applications has revolutionized multiple domains, and one particularly vital area is traffic sign recognition, which significantly enhances driver safety. This paper presents an approach to traffic sign recognition specifically designed for the Jetson Nano 2GB device. By utilizing the YOLOv8 Nano model, the proposed approach achieves a remarkable frame rate of up to 32 frames per second (FPS). To optimize inference speed on Jetson with limited memory, the approach incorporates TensorRT and quantization techniques. In addition, this paper introduces a dataset called the Vietnamese Traffic Sign Detection Database 100 (VTSDB100). This dataset is an extension of the VTSDB46 dataset and encompasses a comprehensive collection of 100 different classes of traffic signs …
Recommended citation: Phuong-Nam Tran, Nhat Truong Pham, Nam Van Hai Phan, Duc Tai Phane, Cuong Tuan Nguyen, Duc Ngoc Minh Dang. (2024). "Towards real-time vietnamese traffic sign recognition on embedded systems." 2024 15th International Conference on Information and Communication Technology Convergence (ICTC), 1-6.
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