Year: 2024
Type: article
Abstract: In the realm of expressway development, the importance of vehicle identification technology is steadily rising.However, conventional systems often struggle to maintain optimal recognition accuracy in adverse weather conditions such as heavy rain, snow, or haze.This study proposes an enhanced approach to license plate recognition utilizing radar fusion technology to synthesize multiple information bands and improve image visibility.By addressing the challenges posed by inclement weather, our algorithm aims to overcome limitations observed in conventional de-fogging methods.Through cooperative image processing, the proposed algorithm automatically identifies and enhances license plate regions, subsequently employing a character recognition model to identify clipped characters.This enhanced technique effectively mitigates the impact of complex backgrounds and noise, thus boosting recognition accuracy.Simulation analyses conducted in MATLAB validate the efficacy of our approach.Through simulation analysis, it is found that the recognition accuracy rate can reach 97%, demonstrating its superior recognition accuracy even in adverse weather conditions.
Authors: Andi Gao, Guanglei Zhang
Institution: Krirk University
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $950
Funder: Shandong University
OpenAlex ID: W4402925595