A real-time traffic sign detection in intelligent transportation system using YOLOv8-based deep learning approach

Author:

Tang Mingdeng

Publisher

Springer Science and Business Media LLC

Reference18 articles.

1. Zhang, J., Zou, X., Kuang, L.-D., Wang, J., Sherratt, R.S., Yu, X.: CCTSDB 2021: a more comprehensive traffic sign detection benchmark. Human Centric Comput. Inf. Sci. 12, 2022 (2021)

2. Wang, J., Chen, Y., Dong, Z., Gao, M.: Improved YOLOv5 network for real-time multi-scale traffic sign detection. Neural Comput. Appl. 35(10), 7853–7865 (2023)

3. Gu, Y., Si, B.: A novel lightweight real-time traffic sign detection integration framework based on YOLOv4. Entropy 24(4), 487 (2022)

4. Liang, T., Bao, H., Pan, W., Pan, F.: Traffic sign detection via improved sparse R-CNN for autonomous vehicles. J. Adv. Transp. 2022, 1–16 (2022)

5. Ang, M.C., Sundararajan, E., Ng, K.W., Aghamohammadi, A., Lim, T.L.: Investigation of threading building blocks framework on real time visual object tracking algorithm. Appl. Mech. Mater. 666, 240–244 (2014)

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