Skip to main content
SpringerLink
Log in

Traffic Sign Detection and Recognition Using Dense Connections in YOLOv4

  • Conference paper
  • First Online:
Computer Vision and Image Processing (CVIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1777))

Included in the following conference series:

  • 393 Accesses

Abstract

A self-driving car is a growing technology in India where detection of traffic sign in an unconstrained environment is a challenging task due to its small size. With the development of deep neural networks, many models for object detection have been developed. In this work, we have used a single-stage detection model YoloV4 with further improvements in detection neck for detection of traffic signs. We have used dense connections in place of normal connections of the model for better feature propagation. This improves the accuracy with less inference time. We have conducted our experiments on bench-marked Chinese traffic sign dataset, Tshigua-Tenscent 100K dataset (TT-100K). We have achieved accuracy of 94.30% with 32 FPS.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://github.com/AlexeyAB/darknet.

References

  1. Gomez-Moreno, H., Maldonado-Bascon, S., Gil-Jimenez, P., Lafuente-Arroyo, S.: Goal Evaluation of Segmentation Algorithms for Traffic Sign Recognition, In: IEEE Transactions on Intelligent Transportation Systems 11, pp. 917–930 (2010)

    Google Scholar 

  2. Ruta, A., Li, Y., Liu, X.: Real-time traffic sign recognition from video by class-specific discriminative features. Pattern Recogn. 43(1), 416–430 (2010)

    Article  MATH  Google Scholar 

  3. Salti, S., Petrelli, A., Tombari, F., Fioraio, N., Stefano, L.D.: Traffic sign detection via interest region extraction. Pattern Recogn. 48, 1039–1049 (2015)

    Article  Google Scholar 

  4. Nguwi, Y.-Y., Kouzani, A.: Automatic Road Sign Recognition Using Neural Networks, In: The 2006 IEEE International Joint Conference on Neural Network Proceedings, pp. 3955–3962, (2006)

    Google Scholar 

  5. Romdhane, N.B., Mliki, H., Hammami, M.: An improved traffic signs recognition and tracking method for driver assistance system In: 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), pp. 1–6 (2016)

    Google Scholar 

  6. Yakimov, P., Fursov, V.: Traffic Signs Detection and tracking using modified Hough transform, In: 2015 12th International Joint Conference on e-Business and Telecommunications (ICETE), pp. 22–28 (2015)

    Google Scholar 

  7. Barnes, N., Zelinsky, A., Fletcher, L.S.: Real-Time speed sign detection using the Radial symmetry detector. IEEE Trans. Intell. Transp. Syst. 9, 322–332 (2008)

    Article  Google Scholar 

  8. Zheng, Z., Zhang, H., Wang, B., Gao, Z.: Robust traffic sign recognition and tracking for advanced driver assistance systems, In: 2012 15th International IEEE Conference on Intelligent Transportation Systems, pp. 704–709 (2012)

    Google Scholar 

  9. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation (2014) arXiv [cs.CV]

    Google Scholar 

  10. Girshick, R.: Fast R-CNN, In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440–1448 (2015)

    Google Scholar 

  11. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017)

    Article  Google Scholar 

  12. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN, In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988 (2017)

    Google Scholar 

  13. Liu, W., et al.: SSD: Single Shot MultiBox Detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

    Chapter  Google Scholar 

  14. Redmon, J., et al.: You Only Look Once: unified, real-time object detection, In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)

    Google Scholar 

  15. Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger, In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6517–6525 (2017)

    Google Scholar 

  16. Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement (2018)

    Google Scholar 

  17. Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y. M.: YOLOv4: optimal speed and accuracy of object detection (2020)

    Google Scholar 

  18. Yang, Y., Luo, H., Xu, H., Wu, F.: Towards real-time traffic sign detection and classification. IEEE Trans. Intell. Transp. Syst. 17, 2022–2031 (2016)

    Article  Google Scholar 

  19. Berkaya, S.K., Gunduz, H., Ozsen, O., Akinlar, C., Gunal, S.: On circular traffic sign detection and recognition. Expert Syst. Appl. 48, 67–75 (2016)

    Article  Google Scholar 

  20. Zaklouta, F., Stanciulescu, B.: Real-Time traffic-sign recognition using tree classifiers. IEEE Trans. Intell. Transp. Syst. 13(4), 1507–1514 (2012)

    Article  Google Scholar 

  21. Gomez-Moreno, H., Maldonado-Bascon, S., Gil-Jimenez, P., Lafuente-Arroyo, S.: Goal evaluation of segmentation algorithms for traffic sign recognition. IEEE Trans. Intell. Transp. Syst. 11, 917–930 (2010)

    Article  Google Scholar 

  22. Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition (2014)

    Google Scholar 

  23. He, K., Zhang, X., Ren, S., Sun, J.: Deep Residual Learning for Image Recognition (2015)

    Google Scholar 

  24. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely Connected Convolutional Networks In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269 (2017)

    Google Scholar 

  25. Kamal, U., Tonmoy, T.I., Das, S., Hasan, M.K.: Automatic traffic sign detection and recognition using Segu-net and a modified Tversky loss function with l1-constraint. IEEE Trans. Intell. Transp. Syst. 21, 1467–1479 (2020)

    Article  Google Scholar 

  26. Tang, Q., Cao, G., Jo, K.-H.: Integrated feature pyramid network with feature aggregation for traffic sign detection. IEEE Access 9, 117784–117794 (2021)

    Article  Google Scholar 

  27. Tabernik, D., Skočaj, D.: Deep learning for large-scale traffic-sign detection and recognition. IEEE Trans. Intell. Transp. Syst. 21(4), 1427–1440 (2020)

    Article  Google Scholar 

  28. Liu, Z., Du, J., Tian, F., Wen, J.: MR-CNN: a multi-scale region-based convolutional neural network for small traffic sign recognition. IEEE Access 7, 57120–57128 (2019)

    Article  Google Scholar 

  29. Jin, Y., Fu, Y., Wang, W., Guo, J., Ren, C., Xiang, X.: Multi-Feature fusion and enhancement single shot detector for traffic sign recognition. IEEE Access 8, 38931–38940 (2020)

    Article  Google Scholar 

  30. Luo, H.-W., Zhang, C.-S., Pan, F.-C., Ju, X.-M.: Contextual-YOLOV3: implement better small object detection based deep learning In: 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), pp. 134–141 (2019)

    Google Scholar 

  31. Wang, L., Zhou, K., Chu, A., Wang, G., Wang, L.: An improved light-weight traffic sign recognition algorithm based on YOLOv4-tiny. IEEE Access 9, 124963–124971 (2021)

    Article  Google Scholar 

  32. Wang, H., Yu, H.: Traffic sign detection algorithm based on improved YOLOv4, pp. 1946–1950 (2020)

    Google Scholar 

  33. Zhu, Z., Liang, D., Zhang, S., Huang, X., Li, B., Hu, S.: Traffic-Sign Detection and Classification in the Wild In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2110–2118 (2016)

    Google Scholar 

  34. Wei, L., Xu, C., Li, S., Tu, X.: Traffic sign detection and recognition using novel center-point estimation and local features. IEEE Access 8, 83611–83621 (2020)

    Article  Google Scholar 

  35. Xiao, D.,Liu, L.: Super-Resolution-Based traffic prohibitory sign recognition, In: 2019 IEEE 21st International Conference on High Performance Computing and Communications IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 2383–2388 (2019)

    Google Scholar 

  36. Liu, L., Wang, Y., Li, K., Li, J.: Focus First: Coarse-to-fine traffic sign detection with stepwise learning. IEEE Access 8, 171170–171183 (2020)

    Article  Google Scholar 

Download references

Acknowledgements

We are thankful to Ministry of Higher Education (MHE) for providing the Teaching Assistantships (TA) to carry out the research work. We would also like to acknowledge Department of Computer Science and Engineering, Indian Institute of Technology Indore, for providing the laboratory support and research facilities to carry out this research work.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Indore, Indore, India

    Swastik Saxena & Somnath Dey

Authors
  1. Swastik Saxena
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Somnath Dey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Swastik Saxena .

Editor information

Editors and Affiliations

  1. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Deep Gupta

  2. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Kishor Bhurchandi

  3. Indian Institute of Technology Ropar, Rupnagar, India

    Subrahmanyam Murala

  4. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  5. Indian Institute of Technology Roorkee, Roorkee, India

    Sanjeev Kumar

Rights and permissions

Reprints and permissions

Copyright information

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

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Saxena, S., Dey, S. (2023). Traffic Sign Detection and Recognition Using Dense Connections in YOLOv4. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-31417-9_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-31416-2

  • Online ISBN: 978-3-031-31417-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation