Research on License Plate Recognition Algorithm in Dark-light Environment Based on Deep Learning

Yunzhi Bai

doi:10.54097/hset.v56i.10579

Authors

Yunzhi Bai

DOI:

https://doi.org/10.54097/hset.v56i.10579

Keywords:

monitoring. Nevertheless, recognition technology, substantially enhanced

Abstract

The advent of intelligent transportation technology has witnessed substantial advancements in license plate recognition technology, thereby assuming a crucial role in contemporary traffic monitoring. Nevertheless, conventional license plate recognition technology is inherently constrained by lighting conditions, severely impeding its nocturnal application. Given the escalating demand for license plate recognition technology in nighttime traffic surveillance, traffic violation documentation, and parking facilities, there exists a significant scholarly impetus to investigate nighttime license plate recognition technology. This study primarily focuses on nocturnal license plate recognition and employs deep learning techniques to execute comprehensive license plate recognition tasks. By leveraging this approach, the accuracy of license plate recognition in low-light environments can be substantially enhanced, thereby fortifying nocturnal traffic supervision and augmenting vehicular safety levels.

Downloads

Download data is not yet available.

References

Coetzee C, Botha C, Weber D. PC based number plate recognition system[C]//IEEE International. Symposium on Industrial Electronics. Proceedings. ISIE'98 (Cat. No. 98TH8357). IEEE, 1998, 2: 605-610.

Liu D Y, Song H, Pan Q. License Plate Recognition Based on Neural Network Algorithm to Improve Research[C]//Advanced Materials Research. Trans Tech Publications, 2014, 860: 2892-2897.

Ma S, Fan Y, Lei T, et al. A practical license plate recognition method based on multi-feature extraction [J]. Computer Application Research, 2013, 30 (11): 3495-3499.

Du W. Research on license plate recognition algorithm based on machine learning [D]. Shenyang Normal University, 2017.

Zherzdev S, Gruzdev A. LPRNet: License Plate Recognition via Deep Neural Networks[J]. ar Xiv preprint ar Xiv:1806.10447, 2018.

Peixoto S P, Cámara-Chávez G, Menotti D, et al. Brazilian license plate character recognition using deep learning[C]//Proc. of XI Workshop de Visão Computacional. 2015.

Polishetty R, Roopaei M, Rad P. A next-generation secure cloud-based deep learning license plate recognition for smart cities[C]// 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2016: 286-293.

Abdullah S N H S, Khalid M, Yusof R, et al. Comparison of feature extractors in license plate recognition[C]//First Asia International Conference on Modelling & Simulation (AMS'07). IEEE, 2007: 502-506.

Cui Y, Huang Q. Automatic license extraction from moving vehicles[C]//Proceedings of International Conference on Image Processing. IEEE, 1997, 3: 126-129.

Masood S Z, Shu G, Dehghan A, et al. License plate detection and recognition using deeply learned convolutional neural networks[J]. ar Xiv preprint ar Xiv:1703.07330, 2017.

Ha P S, Shakeri M. License Plate Automatic Recognition based on edge detection[C]//2016 Artificial Intelligence and Robotics (IRANOPEN). IEEE, 2016: 170-174.

Mullot R, Olivier C, Bourdon J L, et al. Automatic extraction methods of container identity number and registration plates of cars[C]// Proceedings IECON'91: 1991 International Conference on Industrial Electronics, Control and Instrumentation. IEEE, 1991: 1739-1744.

Bolotova Y A, Druki A A, Spitsyn V G. License plate recognition with hierarchical temporal memory model[C]//2014 9th International Forum on Strategic Technology (IFOST). IEEE, 2014: 136-139.

D. F. Llorca et al., “Two-camera based accurate vehicle speed measurement using average speed at a fixed point” Proc. 19th Int. Conf. Intell. Transp. Syst., pp. 2533-2538, Nov. 2016.