Deep Learning Approach for Vehicle Number Plate Recognition System with Image Enhancement Technique
Keywords:CNN, Alexnet, MobileNetV2, recognition, detection, convolutional
The number of automobiles and trucks on the road is continually rising, particularly in direct connection to the emergence of the industrial revolution and the expansion of the economy. Because of the proliferation of motor vehicles, there is a greater potential for the violation of traffic laws, which in turn increases the risk of both unintended collisions and criminal activity on the road. In order to address these problems, a sophisticated traffic monitoring system is required. The intelligent technology has the ability to significantly contribute to traffic management via the recognition of licence plates. As part of this work, we use convolutional neural networks (CNNs) based Alexnet and MobileNetV2 framework, a subset of the deep learning technology known as convolutional neural networks, to build a system for the automatic identification and recognition of licence plates. We also propose to improve both the frameworks by applying image enhancement techniques. Both the detection and identification of licence plates are integral parts of this system. A digital camera is used to capture the image of the vehicle during the detection phase. The Alexnet and MobileNetV2 framework then isolates only the licence plate from the whole image. After the licence plate number region has been removed, the low-resolution image is converted into a high-quality one by a process called super resolution. The convolutional layer of a CNN is used in conjunction with a super resolution technique to restore the original image's pixel quality. To separate the characters of a licence plate number, we employ a bounding box method. Features are extracted and labelled using the CNN technique during the recognition stage.
D. Shan, M. Ibrahim, M. Shehata, and W. Badawy, “Automatic license plate recognition (ALPR): a state-of-the-art review,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 2, pp. 311–325, 2012.
C.-C. Tsai, C.-K. Tseng, H.-C. Tang, and J.-I. Guo, “Vehicle detection and classification based on deep neural network for intelligent transportation applications,” in 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 1605–1608, Honolulu, HI, USA, 2018.
S. M. Silva and C. R. Jung, “License plate detection and recognition in unconstrained scenarios,” in Proceedings of the European Conference on Computer Vision (ECCV), pp. 580–596, Munich, Germany, 2018.
Y. Yuan, W. Zou, Y. Zhao, X. Wang, H. Xuefeng, and N. Komodakis, “A robust and efficient approach to license plate detection,” IEEE Transactions on Image Processing, vol. 26, no. 3, pp. 1102–1114, 2017.
R. Panahi and I. Gholampour, “Accurate detection and recognition of dirty vehicle plate numbers for high-speed applications,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 4, pp. 767–779, 2017.
M. Y. Arafat, A. S. M. Khairuddin, and R. Paramesran, “Connected component analysis integrated edge based technique for automatic vehicular license plate recognition framework,” IET Intelligent Transport Systems, vol. 14, no. 7, pp. 712–723, 2020.
S. Yu, B. Li, Q. Zhang, C. Liu, and M. Q.-H. Meng, “A novel license plate location method based on wavelet transform and EMD analysis,” Pattern Recognition, vol. 48, no. 1, pp. 114–125, 2015.
M. S. Al-Shemarry, Y. Li, and S. Abdulla, “Ensemble of Adaboost cascades of 3L-LBPs classifiers for license plates detection with low quality images,” Expert Systems with Applications, vol. 92, pp. 216–235, 2018.
A. M. Al-Ghaili, S. Mashohor, A. R. Ramli, and A. Ismail, “Vertical-edge-based car-license-plate detection method,” IEEE Transactions on Vehicular Technology, vol. 62, no. 1, pp. 26–38, 2013.
R. Azad, F. Davami, and B. Azad, “A novel and robust method for automatic license plate recognition system based on pattern recognition,” Advances in Computer Science: an International Journal, vol. 2, no. 3, pp. 64–70, 2013.
A. Mousa, “Canny edge-detection based vehicle plate recognition,” International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 5, no. 3, pp. 1–8, 2012.
P. Dollár, T. Zhuowen, P. Perona, and S. Belongie, “Integral channel features,” in Proceedings of the British Machine Vision Conference, BMVC Press, London, 2009.
B. Yang, J. Yan, Z. Lei, and S. Z. Li, “Aggregate channel features for multi-view face detection,” in IEEE International Joint Conference on Biometrics, pp. 1–8, FL, USA, 2014.
S. Ghofrani and M. Rasooli, “Farsi license plate detection and recognition based on characters features,” Majlesi Journal of Electrical Engineering, vol. 5, no. 17, pp. 44–51, 2011.
R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587, Columbus, OH, USA, 2014.
M. M. Dehshibi and R. Allahverdi, “Persian vehicle license plate recognition using multiclass Adaboost,” International Journal of Computer and Electrical Engineering, vol. 4, no. 3, pp. 355–358, 2012.
D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004.
H. Bay, T. Tuytelaars, and L. Van Gool, “Surf: speeded up robust features,” in European Conference on Computer Vision, pp. 404–417, Springer, 2006.
T. Björklund, A. Fiandrotti, M. Annarumma, G. Francini, and E. Magli, “Automatic license plate recognition with convolutional neural networks trained on synthetic data,” in 2017 IEEE 19th International Workshop on Multimedia Signal Processing (MMSP), pp. 1–6, Luton, UK, 2017.
V. H. Pham, P. Q. Dinh, and V. H. Nguyen, “CNN-based character recognition for license plate recognition system,” in Asian Conference on Intelligent Information and Database Systems, pp. 594–603, Springer, 2018.
C. Henry, S. Y. Ahn, and S.-W. Lee, “Multinational license plate recognition using generalized character sequence detection,” Access, vol. 8, pp. 35185–35199, 2020.
C. Gou, K. Wang, Y. Yao, and Z. Li, “Vehicle license plate recognition based on extremal regions and restricted Boltzmann machines,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 4, pp. 1096–1107, 2016.
W. Wang, J. Yang, M. Chen, and P. Wang, “A light CNN for end-to-end car license plates detection and recognition,” IEEE Access, vol. 7, pp. 173875–173883, 2019.
G. O. N. G. Wen-bin, S. H. I. Zhang-song, and J. I. Qiang, “Non-segmented Chinese license plate recognition algorithm based on deep neural networks,” in 2020 Chinese Control and Decision Conference (CCDC), pp. 66–71, Hefei, China, 2020.
H. Li, P. Wang, M. You, and C. Shen, “Reading car license plates using deep neural networks,” Image and Vision Computing, vol. 72, pp. 14–23, 2018.
O. Bulan, V. Kozitsky, P. Ramesh, and M. Shreve, “Segmentation- and annotation-free license plate recognition with deep localization and failure identification,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 9, pp. 2351–2363, 2017.
H. Li, P. Wang, and C. Shen, “Toward end-to-end car license plate detection and recognition with deep neural networks,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 3, pp. 1126–1136, 2019.
C. L. P. Chen and B. Wang, “Random-positioned license plate recognition using hybrid broad learning system and convolutional networks,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 1, pp. 444–456, 2020.
S.-L. Chen, C. Yang, J.-W. Ma, F. Chen, and X.-C. Yin, “Simultaneous end-to-end vehicle and license plate detection with multi-branch attention neural network,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 9, pp. 3686–3695, 2020.
F. Gao, Y. Cai, Y. Ge, and S. Lu, “EDF-LPR: a new encoder–decoder framework for license plate recognition,” IET Intelligent Transport Systems, vol. 14, no. 8, pp. 959–969, 2020.
W. Weihong and T. Jiaoyang, “Research on license plate recognition algorithms based on deep learning in complex environment,” IEEE Access, vol. 8, pp. 91661–91675, 2020.
S. Zhang, G. Tang, Y. Liu, and H. Mao, “Robust license plate recognition with shared adversarial training network,” IEEE Access, vol. 8, pp. 697–705, 2020.
R. Girshick, “Fast R-CNN,” in Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448, Santiago, Chile, 2015.
M. Nejati, A. Majidi, and M. Jalalat, “License plate recognition based on edge histogram analysis and classifier ensemble,” in 2015 Signal Processing and Intelligent Systems Conference (SPIS), pp. 48–52, Tehran, Iran, 2015.
K. Deb, V. V. Gubarev, and K.-H. Jo, “Vehicle license plate detection algorithm based on color space and geometrical properties,” in International Conference on Intelligent Computing, pp. 555–564, Springer, 2009.
J. Pirgazi, A. G. Sorkhi, and M. M. P. Kallehbasti, “An efficient robust method for accurate and real-time vehicle plate recognition,” Journal of Real-Time Image Processing, vol. 18, no. 5, pp. 1759–1772, 2021.
D. Erhan, C. Szegedy, A. Toshev, and D. Anguelov, “Scalable object detection using deep neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2147–2154, Columbus, OH, USA, 2014.
C. Szegedy, S. Reed, D. Erhan, D. Anguelov, and S. Ioffe, “Scalable, high-quality object detection,” 2014.
Y. Wen, Y. Lu, J. Yan, Z. Zhou, K. M. von Deneen, and P. Shi, “An algorithm for license plate recognition applied to intelligent transportation system,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 3, pp. 830–845, 2011.
J. Jiao, Q. Ye, and Q. Huang, “A configurable method for multi-style license plate recognition,” Pattern Recognition, vol. 42, no. 3, pp. 358–369, 2009.
Zhen-Xue Chen, Cheng-Yun Liu, Fa-Liang Chang, and Guo-You Wang, “Automatic license-plate location and recognition based on feature salience,” IEEE Transactions on Vehicular Technology, vol. 58, no. 7, pp. 3781–3785, 2009.
Jing-Ming Guo and Yun-Fu Liu, “License plate localization and character segmentation with feedback self-learning and hybrid binarization techniques,” IEEE Transactions on Vehicular Technology, vol. 57, no. 3, pp. 1417–1424, 2008.
N. Omar, A. Sengur, and S. G. S. Al-Ali, “Cascaded deep learning-based efficient approach for license plate detection and recognition,” Expert Systems with Applications, vol. 149, article 113280, 2020.
A. Tourani, A. Shahbahrami, S. Soroori, S. Khazaee, and C. Y. Suen, “A robust deep learning approach for automatic Iranian vehicle license plate detection and recognition for surveillance systems,” IEEE Access, vol. 8, pp. 201317–201330, 2020.
D. M. Izidio, A. Ferreira, H. R. Medeiros, and E. N. Barros, “An embedded automatic license plate recognition system using deep learning,” Design Automation for Embedded Systems, vol. 24, pp. 23–43, 2020.
S. Alghyaline, “Real-time Jordanian license plate recognition using deep learning,” Journal of King Saud University-Computer and Information Sciences, vol. 32, 2020.
Hendry and R.-C. Chen, “Automatic license plate recognition via sliding-window darknet-YOLO deep learning,” Image and Vision Computing, vol. 87, pp. 47–56, 2019.
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