Abstract
The advent of intelligent transportation technology has witnessed substantial advancements in license plate recognition technology, thereby assuming a crucial role in contemporary traffic monitoring. Nevertheless, conventional license plate recognition technology is inherently constrained by lighting conditions, severely impeding its nocturnal application. Given the escalating demand for license plate recognition technology in nighttime traffic surveillance, traffic violation documentation, and parking facilities, there exists a significant scholarly impetus to investigate nighttime license plate recognition technology. This study primarily focuses on nocturnal license plate recognition and employs deep learning techniques to execute comprehensive license plate recognition tasks. By leveraging this approach, the accuracy of license plate recognition in low-light environments can be substantially enhanced, thereby fortifying nocturnal traffic supervision and augmenting vehicular safety levels.
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