Robust Efficient License Plate and Character Detection System Based on Simplified CNN
Robust Efficient License Plate and Character Detection System Based on Simplified CNN
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DOI：
10.1145/3564746.3587108
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Selena He;Tu N. Nguyen;Kun Suo
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文献类型：
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作者：
Selena He;Tu N. Nguyen;Kun Suo
当前的车牌识别系统与降低图像噪声和车牌功能检测过程相处。本文介绍了基于Yolo神经网络的高效且高度准确的车牌检测和角色检测程序，该网络是一个简化的基于CNN的神经网络框架，用于可靠的图像处理系统。与大多数方法不同，我们提出的系统仅需要对数据集进行优先分析，以评估图像内部的潜在噪音，以便程序实现可以更有效，更有针对性地使用Yolo神经网络进行设计和优化。借助我们提出的系统，车牌检测的准确性从63％提高，这是传统图像处理方法执行的，从而提高到90.3％。
Current license plate recognition systems struggle with image noise reduction and license plate feature detecting processes. This paper presents an efficient and highly accurate license plate detection and character detection program based on the YOLO neural network, which is a simplified CNN-based neural network frame for robust image processing systems. Different than most approaches, the system we proposed simply requires a prioritized analysis of the dataset in order to evaluate potential noises inside images so that program implementations could be more effective and more targeted to design and optimize with YOLO neural network. With our presented system, the accuracy of license plate detection improves from 63% which is performed by traditional image processing methods to 90.3%.