Skip to main content

Advertisement

Log in

LIVDN: low illumination vehicle detection network

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Detecting vehicles under low illumination conditions poses a significant challenge due to reduced visibility and lack of contrast. To address this issue, this paper proposes a Low Illumination Vehicle Detection Network (LIVDN). LIVDN utilizes the Dilation-Wise Residual module to enhance the feature extraction network, allowing for a more comprehensive capture of contextual information. The Bidirectional Cascade Feature Fusion module improves detection capabilities for vehicles of various sizes. Additionally, a Bi-level Routing Spatial Attention module directs the network’s attention to vehicle texture features and color information, enhancing detection accuracy. The proposed method is validated on the BDD100K dataset and KITTI dataset. Experimental results demonstrate a significant improvement in vehicle detection accuracy under low illumination conditions.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /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 excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Explore related subjects

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

Data availibility

No datasets were generated or analysed during the current study.

References

  1. Velez, G., Otaegui, O.: Embedding vision-based advanced driver assistance systems: a survey. IET Intel. Transport Syst. 11(3), 103–112 (2017)

    Article  MATH  Google Scholar 

  2. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14, pp. 21–37 (2016). Springer

  3. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 779–788 (2016)

  4. Lv, W., Xu, S., Zhao, Y., Wang, G., Wei, J., Cui, C., Du, Y., Dang, Q., Liu, Y.: Detrs beat yolos on real-time object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 16965-16974) (2023)

  5. Hoanh, N., Pham, T.V.: A multi-task framework for car detection from high-resolution uav imagery focusing on road regions. IEEE Transactions on Intelligent Transportation Systems, 1–14 (2024)

  6. Ying, Z., Zhou, J., Zhai, Y., Quan, H., Li, W., Genovese, A., Piuri, V., Scotti, F.: Large-scale high-altitude uav-based vehicle detection via pyramid dual pooling attention path aggregation network. IEEE Transactions on Intelligent Transportation Systems (2024)

  7. Chang, Y., Jung, C., Ke, P., Song, H., Hwang, J.: Automatic contrast-limited adaptive histogram equalization with dual gamma correction. IEEE Access 6, 11782–11792 (2018)

    Article  MATH  Google Scholar 

  8. Chen, Y.-L.: Nighttime vehicle light detection on a moving vehicle using image segmentation and analysis techniques. WSEAS Trans. Comput. 8(3), 506–515 (2009)

    MathSciNet  MATH  Google Scholar 

  9. O’Malley, R., Jones, E., Glavin, M.: Rear-lamp vehicle detection and tracking in low-exposure color video for night conditions. IEEE Trans. Intell. Transp. Syst. 11(2), 453–462 (2010)

    Article  MATH  Google Scholar 

  10. Dai, X., Liu, D., Yang, L., Liu, Y.: Research on headlight technology of night vehicle intelligent detection based on hough transform. In: 2019 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), 49–52 (2019)

  11. Gao, F., Ge, Y., Lu, S., Zhang, Y.: On-line vehicle detection at nighttime-based tail-light pairing with saliency detection in the multi-lane intersection. IET Intel. Transport Syst. 13(3), 515–522 (2019)

    Article  MATH  Google Scholar 

  12. Chen, X., Chen, H., Xu, H.: Vehicle detection based on multifeature extraction and recognition adopting rbf neural network on adas system. Complexity 2020, 1–11 (2020)

    Article  MATH  Google Scholar 

  13. Parvin, S., Rozario, L.J., Islam, M.E., et al.: Vision-based on-road nighttime vehicle detection and tracking using taillight and headlight features. J. Comput. Commun. 9(03), 29 (2021)

    Article  MATH  Google Scholar 

  14. Zhang, L., Xu, W., Shen, C., Huang, Y.: Vision-based on-road nighttime vehicle detection and tracking using improved hog features. Sensors 24(5), 1590 (2024)

    Article  MATH  Google Scholar 

  15. Xu, Y., Chu, K., Zhang, J.: Nighttime vehicle detection algorithm based on improved faster-rcnn. IEEE Access (2023)

  16. Wang, Z., Zhan, J., Li, Y., Zhong, Z., Cao, Z.: A new scheme of vehicle detection for severe weather based on multi-sensor fusion. Measurement 191, 110737 (2022)

    Article  MATH  Google Scholar 

  17. Vishwakarma, P.K., Jain, N.: Design and augmentation of a deep learning based vehicle detection model for low light intensity conditions. SN Comput. Sci. 5(5), 605 (2024)

    Article  MATH  Google Scholar 

  18. Miao, Y., Liu, F., Hou, T., Liu, L., Liu, Y.: A nighttime vehicle detection method based on yolo v3. In: 2020 Chinese Automation Congress (CAC), 6617–6621 (2020)

  19. Lashkov, I., Yuan, R., Zhang, G.: Edge-computing-facilitated nighttime vehicle detection investigations with clahe-enhanced images. IEEE Trans. Intell Trans. Syst. 24(11), 13370–13383 (2023)

    Article  MATH  Google Scholar 

  20. Li, J., Xiao, D., Yang, Q.: Efficient multi-model integration neural network framework for nighttime vehicle detection. Multimed. Tool. Appl. 81(22), 32675–32699 (2022)

    Article  MATH  Google Scholar 

  21. Kiran, V.K., Dash, S., Parida, P.: Vehicle detection in varied weather conditions using enhanced deep yolo with complex wavelet. Eng. Res. Express 6(2), 025224 (2024)

    Article  Google Scholar 

  22. Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, 2223–2232 (2017)

  23. Shao, X., Wei, C., Shen, Y., Wang, Z.: Feature enhancement based on cyclegan for nighttime vehicle detection. IEEE Access 9, 849–859 (2020)

    Article  MATH  Google Scholar 

  24. Zhou, W., Wang, C., Ge, Y., Wen, L., Zhan, Y.: All-day vehicle detection from surveillance videos based on illumination-adjustable generative adversarial network. IEEE Transactions on Intelligent Transportation Systems (2023)

  25. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) Comput. Vision - ECCV 2020, pp. 213–229. Springer, Cham (2020)

    Chapter  Google Scholar 

  26. Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., Chen, J.: Detrs beat yolos on real-time object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 16965–16974 (2024)

  27. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778 (2016)

  28. Wei, H., Liu, X., Xu, S., Dai, Z., Dai, Y., Xu, X.: Dwrseg: Rethinking efficient acquisition of multi-scale contextual information for real-time semantic segmentation. arXiv preprint arXiv:2212.01173 (2022)

  29. Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2117-2125) (2017)

  30. Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 8759-8768) (2018)

  31. Tan, M., Pang, R., Le, Q.V.: Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10781–10790 (2020)

  32. Zhu, L., Wang, X., Ke, Z., Zhang, W., Lau, R.W.: Biformer: Vision transformer with bi-level routing attention. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10323–10333 (2023)

  33. Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), 3–19 (2018)

  34. Yu, F., Chen, H., Wang, X., Xian, W., Chen, Y., Liu, F., Madhavan, V., Darrell, T.: Bdd100k: A diverse driving dataset for heterogeneous multitask learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2636–2645 (2020)

  35. Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: The kitti dataset. The Int. J. Robot. Res. 32(11), 1231–1237 (2013)

    Article  Google Scholar 

  36. Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, 2980–2988 (2017)

  37. Wang, N., Gao, Y., Chen, H., Wang, P., Tian, Z., Shen, C., Zhang, Y.: Nas-fcos: Fast neural architecture search for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 11943–11951 (2020)

  38. Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L.M., Shum, H.-Y.: Dino: Detr with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:2203.03605 (2022)

Download references

Funding

This research has been supported by Science and Technology Development Plan Project of Jilin Province, China (Grant No. 20240304145SF). The authors also want to thank Changchun Computing Center for providing inclusive computing power and technical support during the completion of this paper.

Author information

Authors and Affiliations

Authors

Contributions

Lan Liu and Fei Yan conceived and designed the study;Yuzhuo Shen and Siyu Li were responsible for collecting and analyzing the data;Yunqing Liu provided guidance and oversight throughout. All authors contributed to the article and approved the submitted version.

Corresponding author

Correspondence to Fei Yan.

Ethics declarations

Conflict of interest

The authors have no Conflict of interest to declare that are relevant to the content of this article.

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

Liu, L., Yan, F., Shen, Y. et al. LIVDN: low illumination vehicle detection network. SIViP 19, 44 (2025). https://doi.org/10.1007/s11760-024-03635-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11760-024-03635-x

Keywords

Navigation