Abstract
At present, Automatic License Plate Recognition(ALPR) technology has been widely used in residential parking, high-speed intersection toll stations, roadside illegal parking, smart transportation and other fields. Although automatic license plate technology has been widely used in various fields, at present, whether it is commercial or academic methods, it is to explore the license plate recognition research of approximate frontal images in specific regions or specific countries (such as China, Brazil, and the United States). Aiming at real and complex scenarios, this paper builds a dataset for countries along the Belt and Road (such as Kenya, Nigeria, Togo, Ghana, etc.), called BR-ALPR dataset, designed to ALPR. We use yolov3 to complete the license plate detection. For license plate recognition, we use an improved Convolutional Recurrent Neural Network (CRNN) algorithm, which inserts the Spatial Transformation Network (STN) into the CRNN. In addition, we still use the method of template paste to complete the enhancement of the dataset. Experimental results show that our method is superior to advanced commercial methods for the detection and recognition of license plates in complex real Scenarios.
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References
Huang, D.S.: Systematic Theory of Neural Networks for Pattern Recognition. Publishing House of Electronic Industry of China, Beijing (1996)
Wu, D., Zheng, S., Yuan, C., Huang, D.S.: A deep model with combined losses for person re-identification. Cogn. Syst. Res. 54, 74–82 (2019)
Huang, D.S.: Radial basis probabilistic neural networks: model and application. Int. J. Pattern Recogn. Artif. Intell. 13(7), 1083–1101 (1999)
Wang, X.F., Huang, D.S.: A novel density-based clustering framework by using level set method. Knowl. Data Eng. 21(11), 1515–1531 (2009)
Wu, Y.: Person re-identification by multi-scale feature representation learning with random batch feature mask. IEEE Trans. Cogn. Develop. Syst. (2020). https://doi.org/10.1109/TCDS.2020.3003674
Zhao, Z.Q., Huang, D.S., Sun, B.Y.: Human face recognition based on multiple features using neural networks committee. Pattern Recogn. Lett. 25(12), 1351–1358 (2004)
Wu, D., Shen, Z., Yuan, C., Huang, D.S: A deep model with combined losses for person re-identification. Cogn. Syst. Res. 54, 74–82 (2018)
Li, B., Huang, D.S.: Locally linear discriminant embedding: An efficient method for face recognition. Pattern Recogn. 41(12), 3813–3821 (2008)
Huang, D.S., Du, J.X.: A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks. Neural Netw. 19(12), 2099–2115 (2008)
Silva, S.M., Jung, C.R.: License Plate Detection and Recognition in Unconstrained Scenarios. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11216, pp. 593–609. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01258-8_36
Laroca, R., Severo, E., Zanlorensi, L.A.: A robust real-time automatic license plate recognition based on the YOLO detector. In: 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1–10 (2018)
Xu, Z., Yang, W., Meng, A.: Towards end-to-end license plate detection and recognition: a large dataset and baseline. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 255–271 (2018)
Li, H., Wang, P., Shen, C.: Toward end-to-end car license plate detection and recognition with deep neural networks. IEEE Transa. Intell. Transp. Syst. 20(3), 1126–1136 (2018)
Lin, C., Wu, C.: A lightweight, high-performance multi-angle license plate recognition model. In: 2019 International Conference on Advanced Mechatronic Systems (ICAMechS), pp. 235–240 (2019)
Redmon, J., Divvala, S., Girshick, R.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(11), 2298–2304 (2016)
Jaderberg, M., Simonyan, K., Zisserman, A., kavukcuoglu, k.: Spatial transformer networks (2015). https://arxiv.org/abs/1506.02025
Huang, D.S.: Application of generalized radial basis function networks to recognition of radar targets. Int. J. Pattern Recogn. Artif. Intell. 13(6), 945–962 (1999)
Huang, D.S., Chi, Z.R., Siu, W.C: A case study for constrained learning neural root finders. Appl. Math. Comput. 165(3), 699–718 (2005)
Shang, L., Huang, D.S., Du, J.X., Zheng, C.H.: Palmprint recognition using Fast ICA algorithm and radial basis probabilistic neural network. Neurocomputing 69(13–15), 1782–1786 (2006)
Huang, D.S., Zhao, W.B.: Determining the centers of radial basis probabilistic neural networks by recursive orthogonal least square algorithms. Appl. Math. Comput. 162(1), 461–473 (2005)
Huang, D.S.: The local minima free condition of feedforward neural networks for outer-supervised learning. IEEE Trans. Syst. Man Cybern. 28(3), 477–480 (1998)
Huang, D.S., Horace, H.S., Chi, Z.R., Wong, H.S.: Dilation method for finding close roots of polynomials based on constrained learning neural networks. Phys. Lett. 309(5), 443–451 (2003)
Wang, X.F., Huang, D.S., Xu, H.: An efficient local Chan-Vese model for image segmentation. Pattern Recogn. 43(3), 603–618 (2010)
Huang, D.S.: The united adaptive learning algorithm for the link weights and the shape parameters in RBFN for pattern recognition. Int. J. Pattern Recogn. Artif. Intell. 11(6), 873–888 (1997)
Huang, D.S.: Systematic Theory of Neural Networks for Pattern Recognition (in Chinese). Publishing House of Electronic Industry of China (1996)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010). https://doi.org/10.1109/TPAMI.2009.167
Ren, S., He, K., Girshick, R.: Faster r-cnn: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Redmon, J., Farhadi, A.: YOLOv3: an incremental Improvement (2018)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6517–6525. IEEE (2017). http://dx.doi.org/10.1109/CVPR.2017.690
He, K., Zhang, X., Ren, S.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. Computer Science (2014)
Lin, C., Wu, C.: A lightweight, high-performance multi-angle license plate recognition model. In: 2019 International Conference on Advanced Mechatronic Systems (ICAMechS), pp. 235–240 (2019)
Xu, Z., et al.: Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 261–277. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01261-8_16
Li, H., Wang, P., Shen, C.: Toward end-to-end car license plate detection and recognition with deep neural networks. IEEE Trans. Intell. Transp. Syst. 20(3), 1126–1136 (2018)
Graves, A., Liwicki, M., Fernandez, S.: A novel connectionist system for unconstrained handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009)
Gonçalves, G.R.: Errata: benchmark for license plate character segmentation. J. Electron. Imaging. 25(6), 69801 (2016)
Masood, S.Z., Shu, G., Dehghan, A., Ortiz, E.G.: License plate detection and recognition using deeply learned convolutional neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2017)
OpenALPR Cloud API. http://www.openalpr.com/cloud-api.html
Bochkovskiy, A., Wang, C.Y.: YOLOv4: optimal speed and accuracy of object detection, arxiv (2020)
Acknowledgments
This work was supported by the grant of National Key R&D Program of China (No. 2018AAA0100100) and partly supported by National Natural Science Foundation of China (Grant nos. 61520106006, 61861146002, 61772370, 61702371, 61732012, 61932008, 61532008, 61672382, 61772357, and 61672203) and China Postdoctoral Science Foundation (Grant no. 2017M611619) and supported by “BAGUI Scholar” Program and the Scientific & Technological Base and Talent Special Program, GuiKe AD18126015 of the Guangxi Zhuang Autonomous Region of China and supported by Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01), LCNBI and ZJLab.
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Li, Z. et al. (2020). License Plate Detection and Recognition Technology for Complex Real Scenarios. In: Huang, DS., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12463. Springer, Cham. https://doi.org/10.1007/978-3-030-60799-9_21
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DOI: https://doi.org/10.1007/978-3-030-60799-9_21
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