通过整合YOLO v8和OCR技术来提高自动化车辆识别,以实现高精度的车牌检测和识别
Hanae Moussaoui1, Nabil El Akkad1, Mohamed Benslimane2
1Engineering Systems and Applications Laboratory, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco.
Scientific reports
|June 22, 2024
概括
本研究介绍了一种使用YOLO v8和OCR的先进车辆车牌检测和识别系统. 该方法实现了高精度,改善了用于安全和执法应用的车辆识别.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 车辆识别系统对于安全,贸易和执法至关重要.
- 目前的系统依赖于计算机视觉和机器学习来检测和识别车牌.
- 这些系统需要提高准确性和效率.
研究的目的:
- 提出一种新的车牌检测和识别方法.
- 为了利用深度学习 (YOLO v8),图像处理和OCR技术.
- 提高车辆管理,安全和透明度.
主要方法:
- 使用CVAT创建和注释数据集.
- 使用YOLO v8.8进行车牌检测.
- 通过k-means集群,值和形态操作进行图像增强.
- 使用OCR进行字符识别.
主要成果:
- 在车牌检测中实现了99%的准确性.
- 在字符识别方面取得了98%的准确性.
- 与现有方法相比,证明了令人信服的结果.
结论:
- 拟议的方法为汽车车牌识别提供了一个高度准确和高效的解决方案.
- 这种系统可以大大有助于加强车辆管理和安全.
- 整合YOLO v8,图像处理和OCR是一个强大的方法.
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