自动约旦车牌检测和识别系统使用深度学习技术
Tharaa Aqaileh1, Faisal Alkhateeb1,2
1Department of Computer Science, Yarmouk University, Irbid 21163, Jordan.
Journal of imaging
|October 27, 2023
概括
这项研究开发了一种自动化系统,用于使用深度学习识别约旦车辆. 该系统准确检测车牌,识别车辆标志,提高城市交通管理的效率.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 城市化的增加导致车辆数量的激增,使手动识别车牌和标志具有挑战性.
- 自动化车辆识别系统对于城市环境中有效的交通管理和执法至关重要.
研究的目的:
- 开发和评估一个基于深度学习的系统,用于约旦车辆的自动识别.
- 使用转移学习技术准确检测车牌,识别车牌上的字符和识别车辆标志.
主要方法:
- 利用深度学习模型的转移学习:用于车牌和标志检测/字符识别的YOLOv3,以及用于标志识别的VGG16.
- 在四个定制数据集上训练模型,包括约旦车辆图像,车牌和标志.
- 雇员的绩效指标包括精度,回忆,F测量和平均平均精度 (mAP).
主要成果:
- 在车牌检测 (mAP 99.9%) 和字符识别 (F-测量 99.95%) 中实现了高精度.
- 在车辆标识检测 (mAP 99.1%) 和识别 (F-措施 98%) 中表现出强的表现.
- 集成的车牌识别系统实现了99.8%的精度,回忆和F-measure.
结论:
- 开发的深度学习系统有效地自动识别约旦车辆,包括车牌和标志.
- 该系统的高精度证明了其在交通监控和车辆管理中的实际应用潜力.
- 转移学习方法为汽车领域的复杂视觉识别任务提供了强大的解决方案.
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