License Plate Detection and Recognition based on Neural Networks in Complex Environments
الموضوعات : فصلنامه علمی پژوهشی سنجش از دور راداری و نوری و سیستم اطلاعات جغرافیایی.N. . Ameena Bibi 1 , Purru Supriya 2
1 - M.E.,Ph.D., Assistant Professor(ECE)
Government College of Technology,
Coimbatore.
2 - M.E Applied Electronics,
Government College of Technology,
Coimbatore.
الکلمات المفتاحية: Region of interest, License plate recognition, convolutional neural network, Horizontal and vertical projection,
ملخص المقالة :
Now a days due to the rapid advancement of economy around the world the count of vehicles increases day by day. Increase in the number of vehicles causes violation detection, road congestion, accidents at different traffic situations, uneven illumination, lighting and weather conditions. To overcome this issue license plate number is recognized but due to variations in license plate layout, font size of characters, tilted number plates, weather conditions, dirt plate and motion blur license plate recognition becomes difficult. License plate recognition has two main tasks, one is to detect the license plate and the other is to identify the license plate characters. By using region of interest license plate is detected. For recognition first tilted images are corrected using affine transformation and to improve the quality of a low-resolution image super resolution CNN is employed and connected component analysis, horizontal and vertical projection profile area used for separating each individuals characters. Each individual character image is fed to the Convolutional Neural Network (CNN) for character extraction and for classification and the license plate is recognized using convolutional neural networks. The main aim of this paper is to recognize different plate layout with different conditions with minimum data set and less processing time with maximum efficiency.
Arafat, M. Y., Khairuddin, A. S. M., & Paramesran, R. (2018). A vehicular license plate recognition framework for skewed images. KSII Transactions on Internet and Information Systems (TIIS), 12(11), 5522-5540.
Chen, S. L., Yang, C., Ma, J. W., Chen, F., & Yin, X. C. (2019). Simultaneous end-to-end vehicle and license plate detection with multi-branch attention neural network. IEEE Transactions on Intelligent Transportation systems, 21(9), 3686-3695.
Henry, C., Ahn, S. Y., & Lee, S. W. (2020). Multinational License Plate Recognition Using Generalized Character Sequence Detection. IEEE Access, 8, 35185-35199.
Hilario Seibel Siome Golenstein, Andeerson, R. (2018). Super Resolution and License plate Recognition in Low -Quality surveillance Videos. IEEE Access, 5, 20020-20035.
Huang, Q., Cai, Z., & Lan, T. (2021). A Single Neural Network for Mixed Style License Plate Detection and Recognition. IEEE Access, 9, 21777-21785.
Liu, Y., Huang, H., Cao, J., & Huang, T. (2018). Convolutional neural networks-based intelligent recognition of Chinese license plates. Soft Computing, 22(7), 2403-2419.
Li, H., Wang, P., & Shen, C. (2019). Toward end-to-end car license plate detection and recognition with deep neural networks. IEEE Transactions on Intelligent Transportation systems, 20(3), 1126-1136.
Pustokhina, I. V., Pustokhin, D. A., Rodrigues, J. J., Gupta, D., Khanna, A., Shankar, K., ... & Joshi, G. P. (2020). Automatic vehicle license plate recognition using optimal K-means with convolutional neural network for intelligent transportation systems. IEEE Access, 8, 92907-92917.
Shashirangana, J., Padmasiri, H., Meedeniya, D., & Perera, C. (2020). Automated license plate recognition: a survey on methods and techniques. IEEE Access, 9, 11203-11225.
Wang, W., Yang, J., Chen, M., & Wang, P. (2019). A light CNN for end-to-end car license plates detection and recognition. IEEE Access, 7, 173875-173883.
Weihong, W., & Jiaoyang, T. (2020). Research on license plate recognition algorithms based on deep learning in complex environment. IEEE Access, 8, 91661-91675.
