A New U-Net Based License Plate Enhancement Model in Night and Day Images

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12046)


A new trend of smart city development opens up many challenges. One such issue is that automatic vehicle driving and detection for toll fee payment in night or limited light environments. This paper presents a new work for enhancing license plates captured in limited or low light conditions such that license plate detection methods can be expanded to detect images at night. Due to the popularity of Convolutional Neural Network (CNN) in solving complex issues, we explore U-Net-CNN for enhancing contrast of license plate pixels. Since the difference between pixels that represent license plates and pixels that represent background is too due to low light effect, the special property of U-Net that extracts context and symmetric of license plate pixels to separate them from background pixels irrespective of content. This process results in image enhancement. To validate the enhancement results, we use text detection methods and based on text detection results we validate the proposed system. Experimental results on our newly constructed dataset which includes images captured in night/low light/limited light conditions and the benchmark dataset, namely, UCSD, which includes very poor quality and high quality images captured in day, show that the proposed method outperforms the existing methods. In addition, the results on text detection by different methods show that the proposed enhancement is effective and robust for license plate detection.


Text detection License plate detection U-Net Convolutional neural networks Low quality license plate detection 


  1. 1.
    Panahi, R., Gholampour, I.: Accurate detection and recognition of dirty vehicle plate numbers for high speed applications. IEEE Trans. ITS 18, 767–779 (2017)Google Scholar
  2. 2.
    Li, H., Wang, P., Shen, C.: Toward end to end car license plate detection and recognition with deep neural networks. IEEE Trans. ITS 20, 1126–1136 (2019)Google Scholar
  3. 3.
    Lin, C.H., Lin, Y.S., Liu, W.C.: An efficient license plate recognition system using convolutional neural networks. In: Proceedings of the ICASI, pp. 224–227 (2018)Google Scholar
  4. 4.
    Zhou, X., et al.: East: an efficient and accurate scene text detector. In: Proceedings of the CVPR, pp. 2642–2651 (2017)Google Scholar
  5. 5.
    Asif, M.R., Chun, Q., Hussain, S., Fareed, M.S., Khan, S.: Multinational vehicle license plate detection in complex backgrounds. J. Vis. Commun. Image Represent. 46, 176–186 (2017)CrossRefGoogle Scholar
  6. 6.
    Xie, L., Ahmad, W., Jin, L., Liu, Y., Zhang, S.: A new CNN based method for multi-directional car license plate detection. IEEE Trans. ITS 19, 507–517 (2018)CrossRefGoogle Scholar
  7. 7.
    Yuan, Y., Zou, W., Zhao, Y., Wang, X., Hu, X., Komodakis, N.: A robust and efficient approach to license plate detection. IEEE Trans. IP 26, 1102–1114 (2017)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Shemarry, M.S.A., Li, Y., Abdulla, S.: Ensemble of adaboost cascades of 3L-LBPs classifiers for license plated detection with low quality images. ESWA 92, 216–235 (2018)Google Scholar
  9. 9.
    Dong, X., et al.: Fast efficient algorithm for enhancement of low lighting video. In: Proceedings of the ICME, pp. 1–6 (2011)Google Scholar
  10. 10.
    Jiang, X., Yao, H., Zhang, S., Lu, X., Zeng, W.: Night video enhancement using improved dark channel prior. In: Proceedings of the ICIP, pp. 553–557 (2013)Google Scholar
  11. 11.
    Sharma, S., Zuo, J.J., Fang, G.: Contrast enhancement using pixel based image fusion in wavelet domain. In: Proceedings of the FC3I, pp. 285–290 (2016)Google Scholar
  12. 12.
    Rui, W., Guoyu, W.: Medical X-ray image enhancement method based on TV-homomorphic filter. In: Proceeding of the ICIVC, pp. 315–318 (2017)Google Scholar
  13. 13.
    Ravishankar, P., Sharmila, R.S., Rajendran, V.: Acoustic image enhancement using Gaussian and Laplacian pyramid – a multiresolution based technique. Multimedia Tools Appl. 77, 5547–5561 (2018)CrossRefGoogle Scholar
  14. 14.
    Raghunandan, K.S., et al.: Riesz fractional based model for enhancing license plate detection and recognition. IEEE Trans. CSVT 28, 2276–2288 (2018)Google Scholar
  15. 15.
    Zhang, C., Shivakumara, P., Xue, M., Zhu, L., Lu, T., Pal, U.: New fusion based enhancement for text detection in night video footage. In: Hong, R., Cheng, W.-H., Yamasaki, T., Wang, M., Ngo, C.-W. (eds.) PCM 2018. LNCS, vol. 11166, pp. 46–56. Springer, Cham (2018). Scholar
  16. 16.
    Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). Scholar
  17. 17.
    Zamberletti, A., Gallo, I., Noce, L.: Augmented text character proposals and convolutional neural networks for text spotting from scene images. In: Proceedings of the ACPR, pp. 196–200 (2015)Google Scholar
  18. 18.
    Khare, V., Shivakumara, P., Kumar, A., Chan, C.C., Lu, T., Blumenstein, M.: A quad tree based method for blurred and non-blurred video text frame classification through quality measures. In: Proceedings of the ICPR, pp. 4012–4017 (2016)Google Scholar
  19. 19.
    Deng, D., Liu, H., Li, X., Cai, D.: PixelLink: detecting scene text via instance segmentation. In: Proceedings of the AAAI (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer Vision and Pattern Recognition UnitIndian Statistical Institute, KolkataKolkataIndia
  2. 2.Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia
  3. 3.Faculty of Information Technology and Electrical Engineering, IIKNTNUGjøvikNorway
  4. 4.National Key Lab for Novel Software TechnologyNanjing UniversityNanjingChina
  5. 5.Faculty of Engineering and Information TechnologyUniversity of Technology, SydneyUltimoAustralia

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