Improved Preprocessing Strategy under Different Obscure Weather Conditions for Augmenting Automatic License Plate Recognition

Suvodip Som; Pritam Kumar Gayen; Sudip Das

doi:10.46604/peti.2023.10594

Authors

Suvodip Som Department of Electrical Engineering, Kalyani Government Engineering College, Nadia, West Bengal, India
Pritam Kumar Gayen Department of Electrical Engineering, Kalyani Government Engineering College, Nadia, West Bengal, India
Sudip Das Department of Electrical Engineering, JIS College of Engineering, Nadia, West Bengal, India

DOI:

https://doi.org/10.46604/peti.2023.10594

Keywords:

hybrid preprocess, weather-based preprocess, non-local means denoising, dark channel prior, adaptive histogram equalization

Abstract

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.

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