Link-Based Traffic Estimation and Simulation for Road Networks Using Electronic Registration Identification Data

Dong, Xia; Zheng, Linjiang; Tang, Yi; Cai, Xiaolin; Liu, Lu; Sun, Dihua

doi:10.1109/tvt.2022.3171835

Cited by 6 publications

(1 citation statement)

References 44 publications

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“…In modern society, a vehicle's automatic license plate recognition (ALPR) is a prime necessity [1][2][3][4][5]. When a real-time vehicle image is captured, the license plate is detected and recognized to identify the numbers, letters, and other symbols within it.…”

Section: Introductionmentioning

confidence: 99%

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

Som

Gayen

Das³

2023

Proc. eng. technol. innov.

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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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