Araştırma Makalesi
BibTex RIS Kaynak Göster

Development of a Traffic Speed Limit Sign Detection System Based on Yolov4 Network

Yıl 2023, , 66 - 75, 29.10.2023
https://doi.org/10.54569/aair.1184569

Öz

Recent developments in artificial intelligence technologies have accelerated the transition to smart systems in the automotive industry. By anticipating driving conditions, these technologies enable the prevention of driver-related errors and accidents as well as the provision of crucial information to the driver. In this study, an artificial intelligence-based system is designed to provide information to drivers about speed signs on the road in order to support traffic safety. In this system, Yolov4 model is used to achieve high speed and accuracy levels. After the model training, the model was validated and the test results were found to be 98%.

Kaynakça

  • https://eur-lex.europa.eu/eli/reg/2019/2144/oj, Date of access: 29.09.2022
  • Stallkamp, J., Schlipsing, M., Salmen, J., & Igel, C. (2011, July). The German traffic sign recognition benchmark: a multi-class classification competition. In The 2011 international joint conference on neural networks (pp. 1453-1460). IEEE.
  • Dewi, C., Chen, R. C., Liu, Y. T., Liu, Y. S., & Jiang, L. Q. (2020, June). Taiwan stop sign recognition with customize anchor. In Proceedings of the 12th International Conference on Computer Modeling and Simulation (pp. 51-55).
  • Zhu, Z., Liang, D., Zhang, S., Huang, X., Li, B., & Hu, S. (2016). Traffic-sign detection and classification in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2110-2118).
  • Rajendran, S. P., Shine, L., Pradeep, R., & Vijayaraghavan, S. (2019, July). Real-time traffic sign recognition using YOLOv3 based detector. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE.
  • Zuo, Z., Yu, K., Zhou, Q., Wang, X., & Li, T. (2017, June). Traffic signs detection based on faster r-cnn. In 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW) (pp. 286-288). IEEE.
  • https://www.nvidia.com/tr-tr/autonomous-machines/embedded-systems/jetson-nano/product-development/, Date of access: 29.09.2022
  • Bochkovskiy A. 2020. Yolo v4, v3 and v2 for Windows and Linux. https://github.com/AlexeyAB/darknet, Date of access: 29.09.2022
  • Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
  • Wang, C. Y., Liao, H. Y. M., Wu, Y. H., Chen, P. Y., Hsieh, J. W., & Yeh, I. H. (2020). CSPNet: A new backbone that can enhance learning capability of CNN. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops (pp. 390-391).
  • Yu, J., & Zhang, W. (2021). Face mask wearing detection algorithm based on improved YOLO-v4. Sensors, 21(9), 3263.
  • Hu, X., Liu, Y., Zhao, Z., Liu, J., Yang, X., Sun, C., ... & Zhou, C. (2021). Real-time detection of uneaten feed pellets in underwater images for aquaculture using an improved YOLO-V4 network. Computers and Electronics in Agriculture, 185, 106135.
Yıl 2023, , 66 - 75, 29.10.2023
https://doi.org/10.54569/aair.1184569

Öz

Kaynakça

  • https://eur-lex.europa.eu/eli/reg/2019/2144/oj, Date of access: 29.09.2022
  • Stallkamp, J., Schlipsing, M., Salmen, J., & Igel, C. (2011, July). The German traffic sign recognition benchmark: a multi-class classification competition. In The 2011 international joint conference on neural networks (pp. 1453-1460). IEEE.
  • Dewi, C., Chen, R. C., Liu, Y. T., Liu, Y. S., & Jiang, L. Q. (2020, June). Taiwan stop sign recognition with customize anchor. In Proceedings of the 12th International Conference on Computer Modeling and Simulation (pp. 51-55).
  • Zhu, Z., Liang, D., Zhang, S., Huang, X., Li, B., & Hu, S. (2016). Traffic-sign detection and classification in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2110-2118).
  • Rajendran, S. P., Shine, L., Pradeep, R., & Vijayaraghavan, S. (2019, July). Real-time traffic sign recognition using YOLOv3 based detector. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE.
  • Zuo, Z., Yu, K., Zhou, Q., Wang, X., & Li, T. (2017, June). Traffic signs detection based on faster r-cnn. In 2017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW) (pp. 286-288). IEEE.
  • https://www.nvidia.com/tr-tr/autonomous-machines/embedded-systems/jetson-nano/product-development/, Date of access: 29.09.2022
  • Bochkovskiy A. 2020. Yolo v4, v3 and v2 for Windows and Linux. https://github.com/AlexeyAB/darknet, Date of access: 29.09.2022
  • Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
  • Wang, C. Y., Liao, H. Y. M., Wu, Y. H., Chen, P. Y., Hsieh, J. W., & Yeh, I. H. (2020). CSPNet: A new backbone that can enhance learning capability of CNN. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops (pp. 390-391).
  • Yu, J., & Zhang, W. (2021). Face mask wearing detection algorithm based on improved YOLO-v4. Sensors, 21(9), 3263.
  • Hu, X., Liu, Y., Zhao, Z., Liu, J., Yang, X., Sun, C., ... & Zhou, C. (2021). Real-time detection of uneaten feed pellets in underwater images for aquaculture using an improved YOLO-V4 network. Computers and Electronics in Agriculture, 185, 106135.
Toplam 12 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka
Bölüm Araştırma Makalesi
Yazarlar

Semih Selçuk Bu kişi benim 0000-0003-3759-5362

Sefa Beker 0000-0001-7646-546X

Ömer Faruk Boyraz Bu kişi benim 0000-0002-3292-2814

Erken Görünüm Tarihi 23 Ekim 2023
Yayımlanma Tarihi 29 Ekim 2023
Kabul Tarihi 19 Eylül 2023
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

IEEE S. Selçuk, S. Beker, ve Ö. F. Boyraz, “Development of a Traffic Speed Limit Sign Detection System Based on Yolov4 Network”, Adv. Artif. Intell. Res., c. 3, sy. 2, ss. 66–75, 2023, doi: 10.54569/aair.1184569.

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