Finding an effective license plate localization (LPL) method is challenging owing to different conditions during the image acquisition phase. Most existing methods do not consider various low-quality image conditions that exist in real-world situations. Low-quality image conditions mean that an image can have low resolution, plate imperfection effects, variable illumination environments or background objects similar to the license plate (LP). To improve the anti-interference ability and the speed performance of algorithm, this study aims to develop a parallel partial enhancement method based on color differences that demonstrates improved localization performance for blue–white LP images under low-quality conditions. A novel color difference model is exploited to enhance LP areas and filter non-LP areas. Blue–white color ratio and projection analysis are performed to select the exact LP area from the candidates. Moreover, this study develops a parallel version based on a multicore CPU for real-time processing for industrial applications. An image database including 395 low-quality car images captured from various scenes under different conditions is tested for the performance evaluation. The extensive experiments show the effectiveness and efficiency of the proposed approach.

中文翻译：

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一种低质量图像中车牌定位的并行局部增强方法

由于图像采集阶段的条件不同，寻找有效的车牌定位 (LPL) 方法具有挑战性。大多数现有方法没有考虑现实世界中存在的各种低质量图像条件。低质量图像条件意味着图像可能具有低分辨率、车牌缺陷效应、可变照明环境或类似于车牌 (LP) 的背景对象。为了提高算法的抗干扰能力和速度性能，本研究旨在开发一种基于色差的并行局部增强方法，以提高低质量条件下蓝白LP图像的定位性能。利用一种新颖的色差模型来增强 LP 区域和过滤非 LP 区域。执行蓝白色比和投影分析以从候选中选择准确的 LP 区域。此外，本研究开发了一个基于多核 CPU 的并行版本，用于工业应用的实时处理。一个图像数据库包括 395 个在不同条件下从不同场景中捕获的低质量汽车图像，用于性能评估。广泛的实验表明了所提出方法的有效性和效率。