Improved Preprocessing Strategy under Different Obscure Weather Conditions for Augmenting Automatic License Plate Recognition
Keywords:hybrid preprocess, weather-based preprocess, non-local means denoising, dark channel prior, adaptive histogram equalization
Automatic license plate recognition (ALPR) systems are widely used for various applications, including traffic control, law enforcement, and toll collection. However, the performance of ALPR systems is often compromised in challenging weather and lighting conditions. This research aims to improve the effectiveness of ALPR systems in foggy, low-light, and rainy weather conditions using a hybrid preprocessing methodology. The research proposes the combination of dark channel prior (DCP), non-local means denoising (NMD) technique, and adaptive histogram equalization (AHE) algorithms in CIELAB color space. And used the Python programming language comparisons for SSIM and PSNR performance. The results showed that this hybrid approach is not merely robust to a variety of challenging conditions, including challenging weather and lighting conditions but significantly more accurate for existing ALPR systems.
R. L. Abduljabbar and H. Dia, “A Bibliometric Overview of IEEE Transactions on Intelligent Transportation Systems (2000–2021),” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 9, pp. 14066-14087, September 2022.
D. Xia, L. Zheng, Y. Tang, X. Cai, L. Chen, W. Liu, et al., “Link-Based Traffic Estimation and Simulation for Road Networks Using Electronic Registration Identification Data,” IEEE Transactions on Vehicular Technology, vol. 71, no. 8, pp. 8075-8088, August 2022.
C. Mangwani, I. Lad, P. Mandore, R. Kulkarni, T. Lonkar, and M. Kamble, “Automatic Vehicle Entry Control System,” 6th International Conference on Intelligent Computing and Control Systems, pp. 22-28, May 2022.
Y. Yu, H. Liu, Y. Fu, W. Jia, J. Yu, and Z. Yan, “Embedding Pose Information for Multiview Vehicle Model Recognition,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 8, pp. 5467-5480, August 2022.
H. Y. Lin and C. Y. Ho, “Adaptive Speed Bump with Vehicle Identification for Intelligent Traffic Flow Control,” IEEE Access, vol. 10, pp. 68009-68016, June 2022.
S. T. Bhairnallykar and V. Narawade, “Pre-Processing of Multimodal MR Images Using NLM and Histogram Equalization,” International Conference on Electronics and Renewable Systems, pp. 964-968, March 2022.
P. R. Sanap and S. P. Narote, “License Plate Recognition System for Indian Vehicles,” AIP Conference Proceedings, vol. 1324, no. 1, pp. 130-134, November 2010.
S. Kaur, “An Automatic Number Plate Recognition System under Image Processing,” International Journal of Intelligent Systems and Applications, vol. 8, no. 3, pp. 14-25, March 2016.
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, April 2017.
M. S. Al-Shemarry, Y. Li, and S. Abdulla, “An Efficient Texture Descriptor for the Detection of License Plates from Vehicle Images in Difficult Conditions,” IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 2, pp. 553-564, February 2020.
J. Tian, G. Wang, J. Liu, and Y. Xia, “License Plate Detection in an Open Environment by Density-Based Boundary Clustering,” Journal of Electronic Imaging, vol. 26, no. 3, article no. 033017, May 2017.
D. Zheng, Y. Zhao, and J. Wang, “An Efficient Method of License Plate Location,” Pattern Recognition Letters, vol. 26, no. 15, pp. 2431-2438, November 2005.
J. Zhang, Y. Li, T. Li, L. Xun, and C. Shan, “License Plate Localization in Unconstrained Scenes Using a Two-Stage CNN-RNN,” IEEE Sensors Journal, vol. 19, no. 13, pp. 5256-5265, July 2019.
M. Molina-Moreno, I. González-Díaz, and F. Díaz-de-María, “Efficient Scale-Adaptive License Plate Detection System,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 6, pp. 2109-2121, June 2019.
S. Lee, S. Yun S, J. H. Nam, C. S. Won, and S. W. Jung, “A Review on Dark Channel Prior based Image Dehazing Algorithms,” EURASIP Journal on Image and Video Processing, vol. 2016, no. 4, January 2016.
K. He, J. Sun, and X. Tang, “Single Image Haze Removal Using Dark Channel Prior,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 12, pp. 2341-2353, December 2011.
M. Wu, Y. Chen, P. Zhu, and W. Chen, “NLM Parameter Optimization for φ-OTDR Signal,” Journal of Lightwave Technology, vol. 40, no. 17, pp. 6045-6051, September 2022.
C. H. Bahnsen and T. B. Moeslund, “Rain Removal in Traffic Surveillance: Does it Matter?” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 8, pp. 2802-2819, August 2019.
A. L. Hakim and R. Dewi, “Automatic Rain Detection System Based on Digital Images of CCTV Cameras Using Convolutional Neural Network Method,” IOP Conference Series: Earth and Environmental Science, vol. 893, article no. 012048, November 2021.
X. Zhao and C. Wu, “Weather Classification Based on Convolutional Neural Networks,” International Conference on Wireless Communications and Smart Grid, pp. 293-296, August 2021.
Vaddiradhesyam, “Indian Vehicle Dataset,” https://www.kaggle.com/datasets/radhesyam/indian-vehicle-dataset, June 28, 2021.
M. Dong, D. He, C. Luo, D. Liu, and W. Zeng, “A CNN-Based Approach for Automatic License Plate Recognition in the Wild,” Proceedings of the British Machine Vision Conference (BMVA), pp. 175.1-175.12, September 2017.
F. Al-Saqqar, M. Al-Diabat, M. Aloun, and A. M AL-Shatnawi, “Handwritten Arabic Text Recognition Using Principal Component Analysis and Support Vector Machines,” International Journal of Advanced Computer Science and Applications, vol. 10, no. 12, pp. 1-6, 2019.
H. Rahman and G. C. Paul, “Tripartite Sub-Image Histogram Equalization for Slightly Low Contrast Gray-Tone Image Enhancement,” Pattern Recognition, vol. 134, article no. 109043, February 2023.
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