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

Autor:	null Suvodip Som, null Pritam Kumar Gayen, null Sudip Das
Rok vydání:	2023
Předmět:	General Medicine
Zdroj:	Proceedings of Engineering and Technology Innovation. 24:29-40
ISSN:	2518-833X 2413-7146
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::e53afd961e6528b17fe817f10e26567d https://doi.org/10.46604/peti.2023.10594 Zobrazit plný text záznamu