Skip to main content

Advertisement

Springer Nature Link
Log in

A plug-and-play image enhancement model for end-to-end object detection in low-light condition

  • Regular Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

Although object detection algorithms based on deep learning have been widely used in many scenarios, they face challenges under some degraded conditions, such as low-light. A conventional solution is that image enhancement approaches are used as a separate pre-processing module to improve the quality of degraded image. However, this two-step approach makes it difficult to unify the goals of enhancement and detection, that is, low-light enhancement operations are not always helpful for subsequent object detection. Recently, some works try to integrate enhancement and detection in an end-to-end network, but still suffer from complex network structure, training convergence problem and demanding reference images. To address above problems, a plug-and-play image enhancement model is proposed in this paper, namely, low-light image enhancement (LLIE) model, which can be easily embedded into some off-the-shelf object detection methods in an end-to-end manner. LLIE is composed of a parameter estimation module and image processing module. The former learns to regress lighting enhancement parameters according to the feedback of detection network, and the latter enhances degraded image adaptively to promote subsequent detection model under low-light condition. Extensive object detection experiments on several low-light image data sets show that the performance of detector is significantly improved when LLIE is integrated.

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
$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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Data availability

The data sets used in this article are cited with references at their respective place.

References

  1. 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). https://doi.org/10.1109/TPAMI.2016.2577031

    Article  PubMed  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: Proceeding of the European Conference on Computer Vision (ECCV), vol. 9905, pp. 21–37 (2016). https://doi.org/10.1007/978-3-319-46448-0_2

  3. Cai, Z., Vasconcelos, N.: Cascade r-cnn: Delving into high quality object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6154–6162 (2018). https://doi.org/10.1109/CVPR.2018.00644

  4. Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767 (2018)

  5. Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430 (2021)

  6. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proceeding of the European Conference on Computer Vision (ECCV), vol. 12346, pp. 213–229 (2020). https://doi.org/10.1007/978-3-030-58452-8_13

  7. Lore, K.G., Akintayo, A., Sarkar, S.: Llnet: a deep autoencoder approach to natural low-light image enhancement. Pattern Recognit. 61, 650–662 (2017). https://doi.org/10.1016/j.patcog.2016.06.008

    Article  ADS  Google Scholar 

  8. Jiang, Y., Gong, X., Liu, D., Cheng, Y., Fang, C., Shen, X., Yang, J., Zhou, P., Wang, Z.: Enlightengan: deep light enhancement without paired supervision. IEEE Trans. Image Process. 30, 2340–2349 (2021). https://doi.org/10.1109/TIP.2021.3051462

    Article  ADS  PubMed  Google Scholar 

  9. Guo, C., Li, C., Guo, J., Loy, C.C., Hou, J., Kwong, S., Cong, R.: Zero-reference deep curve estimation for low-light image enhancement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1780–1789 (2020). https://doi.org/10.1109/CVPR42600.2020.00185

  10. Yang, W., Wang, S., Fang, Y., Wang, Y., Liu, J.: From fidelity to perceptual quality: a semi-supervised approach for low-light image enhancement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3063–3072 (2020). https://doi.org/10.1109/CVPR42600.2020.00313

  11. Huang, S.-C., Le, T.-H., Jaw, D.-W.: Dsnet: joint semantic learning for object detection in inclement weather conditions. IEEE Trans. Pattern Anal. Mach. Intell. 43(8), 2623–2633 (2021). https://doi.org/10.1109/TPAMI.2020.2977911

    Article  PubMed  Google Scholar 

  12. Chen, Y., Li, W., Sakaridis, C., Dai, D., Van Gool, L.: Domain adaptive faster r-cnn for object detection in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3339–3348 (2018). https://doi.org/10.1109/CVPR.2018.00352

  13. Hnewa, M., Radha, H.: Multiscale domain adaptive yolo for cross-domain object detection. In: 2021 IEEE International Conference on Image Processing (ICIP), pp. 3323–3327 (2021). https://doi.org/10.1109/ICIP42928.2021.9506039

  14. Liu, W., Ren, G., Yu, R., Guo, S., Zhu, J., Zhang, L.: Image-adaptive yolo for object detection in adverse weather conditions. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 1792–1800 (2022)

  15. Kong, T., Yao, A., Chen, Y., Sun, F.: Hypernet: Towards accurate region proposal generation and joint object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 845–853 (2016). https://doi.org/10.1109/CVPR.2016.98

  16. Dai, J., Li, Y., He, K., Sun, J.: R-fcn: Object detection via region-based fully convolutional networks. Advances in neural information processing systems. 29 (2016)

  17. Chen, X., Li, H., Wu, Q., Ngan, K.N., Xu, L.: High-quality r-cnn object detection using multi-path detection calibration network. IEEE Trans. Circuits Syst. Video Technol. 31(2), 715–727 (2021). https://doi.org/10.1109/TCSVT.2020.2987465

    Article  Google Scholar 

  18. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016). https://doi.org/10.1109/CVPR.2016.91

  19. Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017). https://doi.org/10.1109/CVPR.2017.690

  20. Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)

  21. Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 318–327 (2020). https://doi.org/10.1109/TPAMI.2018.2858826

    Article  PubMed  Google Scholar 

  22. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  23. Liang, X., Zhang, J., Zhuo, L., Li, Y., Tian, Q.: Small object detection in unmanned aerial vehicle images using feature fusion and scaling-based single shot detector with spatial context analysis. IEEE Trans. Circuits Syst. Video Technol. 30(6), 1758–1770 (2020). https://doi.org/10.1109/TCSVT.2019.2905881

    Article  Google Scholar 

  24. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015). https://doi.org/10.1109/CVPR.2015.7298594

  25. Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017). https://doi.org/10.1109/CVPR.2017.106

  26. Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., Haar Romeny, B., Zimmerman, J.B., Zuiderveld, K.: Adaptive histogram equalization and its variations. Comput. Vis. Graph. Image Process. 39(3), 355–368 (1987)

    Article  Google Scholar 

  27. Land, E.H.: The retinex theory of color vision. Scient. Am. 237(6), 108–129 (1977)

    Article  CAS  Google Scholar 

  28. Pisano, E.D., Zong, S., Hemminger, B.M., DeLuca, M., Johnston, R.E., Muller, K., Braeuning, M.P., Pizer, S.M.: Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms. J. Digit. Imaging. 11(4), 193–200 (1998). https://doi.org/10.1007/BF03178082

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Chen, B.-H., Wu, Y.-L., Shi, L.-F.: A fast image contrast enhancement algorithm using entropy-preserving mapping prior. IEEE Trans. Circuits Syst. Video Technol. 29(1), 38–49 (2019). https://doi.org/10.1109/TCSVT.2017.2773461

    Article  Google Scholar 

  30. Wang, S., Zheng, J., Hu, H.-M., Li, B.: Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Trans. Image Process. 22(9), 3538–3548 (2013). https://doi.org/10.1109/TIP.2013.2261309

    Article  ADS  PubMed  Google Scholar 

  31. Fu, X., Zeng, D., Huang, Y., Zhang, X.-P., Ding, X.: A weighted variational model for simultaneous reflectance and illumination estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2782–2790 (2016). https://doi.org/10.1109/CVPR.2016.304

  32. Guo, X., Li, Y., Ling, H.: Lime: Low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 26(2), 982–993 (2017). https://doi.org/10.1109/TIP.2016.2639450

    Article  ADS  MathSciNet  Google Scholar 

  33. Li, M., Liu, J., Yang, W., Sun, X., Guo, Z.: Structure-revealing low-light image enhancement via robust retinex model. IEEE Trans. Image Process. 27(6), 2828–2841 (2018). https://doi.org/10.1109/TIP.2018.2810539

    Article  ADS  MathSciNet  PubMed  Google Scholar 

  34. Wei, C., Wang, W., Yang, W., Liu, J.: Deep retinex decomposition for low-light enhancement. In: British Machine Vision Conference, p. 155 (2018)

  35. Polesel, A., Ramponi, G., Mathews, V.J.: Image enhancement via adaptive unsharp masking. IEEE Trans. Image Process. 9(3), 505–510 (2000). https://doi.org/10.1109/83.826787

    Article  ADS  CAS  PubMed  Google Scholar 

  36. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90

  37. Loh, Y.P., Chan, C.S.: Getting to know low-light images with the exclusively dark dataset. Comput. Vis. Image Underst. 178, 30–42 (2019) https://doi.org/10.1016/j.cviu.2018.10.010

  38. Everingham, M., Winn, J.: The pascal visual object classes challenge 2012 (voc2012) development kit. Pattern Anal. Stat. Model. Comput. Learn., Tech. Rep. 2007(1-45), 5 (2012)

  39. Everingham, M., Eslami, S., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes challenge: a retrospective. Int. J. Comput. Vis. 111(1), 98–136 (2015). https://doi.org/10.1007/s11263-014-0733-5

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by the National Natural Science Foundation of China under Grant No.U21B2038, U19B2039, U1811463 and 62172023, Beijing Natural Science Foundation (4222021) and R&D Program of Beijing Municipal Education Commission (KZ202210005008). The authors would like to thank the anonymous reviewers and editors for their constructive comments and suggestions.

Author information

Authors and Affiliations

  1. Faculty of Information Technology, Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, 100124, China

    Jiaojiao Yuan, Yongli Hu, Yanfeng Sun, Boyue Wang & Baocai Yin

Authors
  1. Jiaojiao Yuan
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yongli Hu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yanfeng Sun
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Boyue Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Baocai Yin
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Yuan wrote the main manuscript and conducted related experiments; Hu provided ideas, provided guidance on paper writing and experimental guidance, and provided funding support; Sun, Wang and Yin provided experimental guidance and funding support.

Corresponding author

Correspondence to Yongli Hu.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Communicated by B. Bao.

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

Yuan, J., Hu, Y., Sun, Y. et al. A plug-and-play image enhancement model for end-to-end object detection in low-light condition. Multimedia Systems 30, 27 (2024). https://doi.org/10.1007/s00530-023-01228-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00530-023-01228-1

Keywords