摘 要
随着智能交通系统的快速发展，车牌识别技术作为其核心组成部分，在交通管理、车辆监控及停车收费等领域发挥着重要作用。本研究旨在设计与实现一种高效、准确的车牌识别系统，以满足复杂场景下的实际应用需求。研究基于深度学习框架，结合卷积神经网络（CNN）和光学字符识别（OCR）技术，提出了一种端到端的车牌识别方法。该方法首先通过改进的YOLOv5算法对车牌进行快速定位，随后利用CRNN模型实现字符序列的精确识别，并引入注意力机制优化长车牌的识别效果。实验结果表明，所提出的系统在多种光照条件、天气状况以及遮挡情况下的平均识别率达到97.3%，显著优于传统方法。此外，系统具备实时处理能力，单帧图像处理时间低于20毫秒，适用于高速场景下的车辆信息采集。本研究的主要创新点在于将轻量化网络结构与高性能识别算法相结合，同时针对实际应用场景中的特殊问题提出了针对性解决方案。研究成果为智能交通系统的进一步发展提供了技术支持，并为后续相关研究奠定了基础。
关键词：车牌识别 深度学习 卷积神经网络
Abstract
With the rapid development of intelligent transportation systems, license plate recognition technology, as a core component, plays a significant role in traffic management, vehicle monitoring, and parking fee collection. This study aims to design and implement an efficient and accurate license plate recognition system to meet the demands of practical applications in complex scenarios. Based on deep learning fr ameworks, the study proposes an end-to-end license plate recognition method by integrating convolutional neural networks (CNN) and optical character recognition (OCR) technologies. The method first employs an improved YOLOv5 algorithm for rapid license plate localization and subsequently utilizes a CRNN model for precise character sequence recognition, with an attention mechanism introduced to optimize the recognition performance of longer license plates. Experimental results demonstrate that the proposed system achieves an average recognition accuracy of 97.3% under various lighting conditions, weather situations, and occlusion scenarios, significantly outperforming traditional methods. Moreover, the system is capable of real-time processing, with a single-fr ame image processing time of less than 20 milliseconds, making it suitable for high-speed scenarios involving vehicle information acquisition. The primary innovation of this research lies in combining lightweight network architectures with high-performance recognition algorithms while proposing targeted solutions for specific issues encountered in real-world application scenarios. The research outcomes provide technical support for the further development of intelligent transportation systems and lay a foundation for subsequent related studies.
Keyword:License Plate Recognition Deep Learning Convolutional Neural Network
目 录
1绪论 1
1.1智能交通与车牌识别的研究背景 1
1.2车牌识别系统在智能交通中的意义 1
1.3国内外车牌识别技术研究现状 1
1.4本文研究方法与技术路线 2
2车牌识别系统的需求分析与设计框架 2
2.1智能交通对车牌识别系统的需求 2
2.2车牌识别系统的功能需求分析 3
2.3系统设计的整体框架与模块划分 3
2.4数据采集与预处理的设计要求 3
2.5系统性能指标的定义与评估标准 4
3核心算法与关键技术实现 4
3.1车牌定位算法的设计与优化 5
3.2字符分割技术的研究与实现 5
3.3车牌字符识别算法的选择与改进 5
3.4基于深度学习的识别模型构建 6
3.5算法复杂度与实时性分析 6
4系统开发与测试验证 7
4.1开发环境与工具的选择 7
4.2系统集成与功能实现 7
4.3测试数据集的构建与标注 8
4.4系统性能测试与结果分析 8
4.5实际应用场景下的效果验证 8
结论 9
参考文献 10
致谢 11
