Monocular vehicle speed detection based on improved YOLOX and DeepSORT | Neural Computing and Applications
Skip to main content
Springer Nature Link
Log in

Monocular vehicle speed detection based on improved YOLOX and DeepSORT

  • S.I.: Neural Networks and Machine Learning Empowered Methods and Applications in Healthcare
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

A monocular vehicle speed detection method based on improved YOLOX and DeepSORT is proposed for the simple scene of fixed shooting angle without high precision but requiring control cost. For continuous video frames collected from a monocular fixed perspective, the vehicle is first identified by using the YOLOX object detection network improved by ELAN module and the CAENet attention mechanism constructed by CA attention and ECANet. Then, the DeepSORT target tracking algorithm is used to match the recognition results of the object detection network output in the before and after frames to find the same target in different frames. Finally, a coordinate system transformation algorithm is used to convert the position distance of the target moving in different frame images into the actual ground plane distance and divide it by the detection interval time to obtain the vehicle speed. The experimental results show that our improved object detection model can increase mAP by 2% to 4% compared with YOLOX in different versions. Compared with the original model, the target tracking using the improved YOLOX is improved by 4.3% on MOTA. The speed limiting precision of speed detection is 75% in the corresponding speed range in experimental testing site 1 and the mean error of the effective velocity value measured by our speed measurement method is 2.10 km/h in experimental testing site 2, which is better than the mean error of 5.46 km/h obtained by the radar pistol velocimeter. This detection method enables economical and efficient vehicle speed detection in simple scenes.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

Not applicable.

References

  1. Liang Y, Li KL, Bi FH, Zhang K, Yang J (2020) Research on lfmcw radar velocity ranging optimization system based on fpga - sciencedirect. Procedia Comput Sci 166:187–194

    Article  Google Scholar 

  2. Xiaoyou YU, Chen X, Yang G (2015) A new vehicle-mounted radar range and velocity measurement approach in radar-communication integration system

  3. Mao X, Inoue D, Kato S, Kagami M (2012) Amplitude-modulated laser radar for range and speed measurement in car applications. IEEE Trans Intell Transp Syst 13(1):408–413

    Article  Google Scholar 

  4. Barros J, Oliveira L (2021) Deep speed estimation from synthetic and monocular data. In: 2021 IEEE intelligent vehicles symposium (IV), pp 668–673

  5. Ge Z, Liu S, Wang F, Li Z, Sun J (2021) Yolox: exceeding yolo series in 2021

  6. Wojke N, Bewley A, Paulus D (2017) Simple online and realtime tracking with a deep association metric. In: 2017 IEEE international conference on image processing (ICIP), 3645–3649

  7. Bochkovskiy A, Wang CY, Liao H (2020) Yolov4: optimal speed and accuracy of object detection

  8. Ge Z, Liu S, Li Z, Yoshie O, Sun J (2021) OTA: optimal transport assignment for object detection. In: 2021 IEEE/CVF conference on computer vision and pattern recognition (CVPR), 303–312

  9. Zhang X, Zeng H, Guo S, Zhang L (2022) Efficient long-range attention network for image super-resolution. In: European conference on computer vision

  10. Wang C, Bochkovskiy A, Liao HM (2022) YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv, abs/2207.02696

  11. Hu J, Shen L, Sun G (2017) Squeeze-and-excitation networks. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, 7132–7141

  12. Hou Q, Zhou D, Feng J (2021) Coordinate attention for efficient mobile network design. In: 2021 IEEE/CVF conference on computer vision and pattern recognition (CVPR), 13708–13717

  13. Woo S, Park J, Lee J, Kweon I (2018) CBAM: convolutional block attention module. In: European conference on computer vision

  14. Wang Q, Wu B, Zhu PF, Li P, Zuo W, Hu Q (2019) ECA-net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp 11531–11539

  15. Rezatofighi SH, Tsoi N, Gwak J, Sadeghian A, Reid ID, Savarese S (2019). Generalized intersection over union: a metric and a loss for bounding box regression. In: 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR), 658–666

  16. Zheng Z, Wang P, Liu W, Li J, Ye R, Ren D (2019) Distance-IoU loss: faster and better learning for bounding box regression. In: AAAI conference on artificial intelligence

  17. Bewley A, Ge Z, Ott L, Ramos FT, Upcroft B (2016) Simple online and realtime tracking. In: 2016 IEEE international conference on image processing (ICIP), pp 3464–3468

  18. Lin T, Maire M, Belongie SJ, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft COCO: common objects in context. In: European Conference on Computer Vision

  19. Cao Y, He Z, Wang L, Wang W, Yuan Y, Zhang D, et al (2021) VisDrone-DET2021: the vision meets drone object detection challenge results. In: International conference on computer vision. IEEE

  20. Liu X, Liu W, Mei T, Ma H (2018) Provid: progressive and multimodal vehicle reidentification for large-scale urban surveillance. IEEE Trans Multimedia, 1–1

  21. Liu X, Liu W, Mei T, Ma H (2016) A deep learning-based approach to progressive vehicle re-identification for urban surveillance. Springer, Berlin

    Book  Google Scholar 

  22. Liu X, Wu L, Ma H, Fu H (2016) Large-scale vehicle re-identification in urban surveillance videos. IEEE

  23. Sergios T (2015) Stochastic gradient descent. Mach Learn, 161–231

  24. Lyu S, Chang MC, Carcagni P, Anisimov D, Bochinski E, Galasso F, et al (2017) UA-DETRAC 2017: report of AVSS2017 & IWT4S challenge on advanced traffic monitoring. In: 2018 15th IEEE international conference on advanced video and signal based surveillance (AVSS). IEEE Computer Society

  25. Lyu S, Chang MC, Du D, Li W, Chung YS (2018) UA-DETRAC 2018: report of AVSS2018 and IWT4S challenge on advanced traffic monitoring. In: 2018 15th IEEE international conference on advanced video and signal based surveillance (AVSS). IEEE

  26. Wen L, Du D, Cai Z, Lei Z, Chang MC, Qi H (2020) Ua-detrac: a new benchmark and protocol for multi-object detection and tracking. Academic Press

  27. Zhang Y, Ren W, Zhang Z, Jia Z, Wang L, Tan T (2021) Focal and efficient IOU loss for accurate bounding box regression. Neurocomputing 506:146–157

    Article  Google Scholar 

Download references

Funding

This research was funded by Science and Technology Commission of Shanghai Municipality (Grant No. 22DZ1100803).

Author information

Authors and Affiliations

  1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, Shanghai, China

    Kaiyu Zhang, Fei Wu, Haojun Sun & Meiyu Cai

Authors
  1. Kaiyu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fei Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haojun Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Meiyu Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

KZ provided ideas, conducted code improvements and experiments for object detection and object tracking, as well as experiments for speed detection, and wrote the manuscript. FW provided assistance with the experiments and the revision of the manuscript, and HS and MC wrote the code for the coordinate system transformation.

Corresponding author

Correspondence to Kaiyu Zhang.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Informed consent

Not applicable.

Institutional review board statement

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, K., Wu, F., Sun, H. et al. Monocular vehicle speed detection based on improved YOLOX and DeepSORT. Neural Comput & Applic 36, 9643–9660 (2024). https://doi.org/10.1007/s00521-023-08963-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-08963-6

Keywords

Search

Navigation