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Text Based Traffic Signboard Detection Using YOLO v7 Architecture

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Advances in Computing and Data Sciences (ICACDS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1848))

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Abstract

Recent developments in computer vision and deep learning technology have increased the prevalence of advanced driver assistance systems (ADAS). ADAS technologies aim to reduce traffic accidents and make driving safer. The proposed work is an additional ADAS feature or can help the driver navigate better through roads while focusing more on the roads. The system uses a small camera mounted at the front of the car, and images from that are then fed into the YOLOv7 model, which can run on jetson nano or other such computing hardware. In the proposed model, the results we have achieved have an overall accuracy of 86% with the system and speed at which it can perform efficiently, ranging from object detection to reading the data on the sign boards.

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References

  1. Gábor Balázs, G., Gyulai, C.: “Road type detection based on traffic sign and data”, Article ID 6766455. Journal of Advanced Transportation, Hindawi Sds (2022)

    Google Scholar 

  2. Sahu, N., Sonkusare, M.: A study on optical character recognition technique. Int. J. Comput. Sci. Info. Technol. Cont. Eng. (2017)

    Google Scholar 

  3. Sampada, P.S., Shakeela, A., Singh, S., Supriya, J., Kavya, M.: Traffic sign board recognition and voice alert system using convolution neural network. Int. J. Eng. Res. Technol. (IJERT) RTCSIT 10(12) (2022)

    Google Scholar 

  4. Toaha, S.I., et al.: Automatic signboard detection and localization in densely populated developing cities. Signal Processing: Image Communication 109 (2022)

    Google Scholar 

  5. Sudha, M., Galdis pushparathi, D.: Traffic sign detection and recognition using RGSM and a novel feature extraction method. Peer-to-Peer Netw. Appl. 14, 2026–2037 (2021)

    Google Scholar 

  6. Ciuntu, V., Ferdowsi, H.: Real-time traffic sign detection and classification using machine learning and optical character recognition. IEEE Int. Conf. Electro Info. Technol. (EIT) 2020, 480–486 (2020). https://doi.org/10.1109/EIT48999.2020.9208309

    Article  Google Scholar 

  7. Snegireva, D., Perkova, A.: Traffic sign recognition application using Yolov5 architecture.2021 International Russian Automation Conference (RusAutoCon), Sochi, Russian Federation, pp. 1002–1007 (2021). https://doi.org/10.1109/RusAutoCon52004.2021.9537355

  8. Ren, X., Zhang, W., Wu, M., Li, C., Wang, X.: Meta-YOLO: meta-learning for few-shot traffic sign detection via decoupling dependencies. Appl. Sci. 12, 5543 (2022). https://doi.org/10.3390/app12115543

    Article  Google Scholar 

  9. Srivastava, A.: Fast detection of multiple objects in traffic scenes with a common detection framework. Int. J. Res. Appl. Sci. Eng. Technol. 9, 642–648 (2021). https://doi.org/10.22214/ijraset.2021.37386

  10. Zhu, Y., Liao, M., Yang, M., Liu, W.: Cascaded segmentation-detection networks for text-based traffic sign detection. IEEE Trans. Intell. Transp. Syst. 19(1), 209–219 (2018). https://doi.org/10.1109/TITS.2017.2768827. Jan.

    Article  Google Scholar 

  11. Hu, J., Wang, Z., Chang, M., Xie, L., Xu, W., Chen, N.: PSG-“Yolov5: a paradigm for traffic sign detectionand recognition algorithm based on deep learning.” Symmetry 14, 2262 (2022). https://doi.org/10.3390/sym14112262

    Article  Google Scholar 

  12. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. California Univ San Diego La Jolla Dept of Computer Science and Engineering (2002)

    Google Scholar 

  13. Maldonado-Bascón, S., Lafuente-Arroyo, S., Gil-Jimenez, P., Gómez-Moreno, H., Lómez-Ferreras, F.: Road-sign detection and recognition based on support vector machines. IEEE Trans. Intell. Transp. Syst. 8, 264–278 (2007)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India

    Ananya Negi, Yash Kesarwani & P. Saranya

Authors
  1. Ananya Negi
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  2. Yash Kesarwani
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  3. P. Saranya
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Corresponding author

Correspondence to P. Saranya .

Editor information

Editors and Affiliations

  1. Consilio Research Lab, Tallinn, Estonia

    Mayank Singh

  2. Jaypee University of Engineering and Technology, Guna, India

    Vipin Tyagi

  3. Jaypee University of Information Technology, Waknaghat, India

    P.K. Gupta

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Negi, A., Kesarwani, Y., Saranya, P. (2023). Text Based Traffic Signboard Detection Using YOLO v7 Architecture. In: Singh, M., Tyagi, V., Gupta, P., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2023. Communications in Computer and Information Science, vol 1848. Springer, Cham. https://doi.org/10.1007/978-3-031-37940-6_1

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  • DOI: https://doi.org/10.1007/978-3-031-37940-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37939-0

  • Online ISBN: 978-3-031-37940-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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