用YOLOv8图像分类进行驾驶研究的凝视区分类
Frouke Hermens1, Wim Anker1, Charmaine Noten1
1Department of Computer Science, Open University of the Netherlands, 6419 AT Heerlen, The Netherlands.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
本研究介绍了使用YOLOv8图像分类在驾驶员中检测视线区域的准确,自动化系统. 该方法不需要预处理图像,并且当模型被训练为特定的驱动程序和条件时,可以实现高精度.
科学领域:
- 计算机视觉 计算机视觉
- 道路交通安全 道路交通安全
- 人与计算机的交互
背景情况:
- 视线区域检测对于道路安全研究至关重要,识别驾驶员注意力区域.
- 现有的自动注释视线区域的方法可能很复杂,需要预处理.
- 开发一个准确,用户友好的系统对于实际应用至关重要.
研究的目的:
- 开发和验证使用YOLOv8进行道路安全研究的自动视线区域检测系统.
- 评估YOLOv8在不同数据集和条件下对视线区域分类的准确性.
- 为数据收集和模型培训提供用户友好的应用程序.
主要方法:
- 使用YOLOv8进行驾驶员视线区域的图像分类.
- 在现有和新收集的数据集上测试了系统,视线区域的数量不同 (9,10和12).
- 训练有素的YOLOv8模型,专门针对驾驶员的人口统计和条件 (例如,眼镜,太阳镜).
主要成果:
- 在没有图像预处理的情况下,在视线区域检测中实现了近乎完美的准确性.
- 当YOLOv8模型根据特定的驾驶员和驾驶条件量身定制时,表现出高性能.
- 开发了用于图像收集和YOLOv8模型培训/应用的伴侣应用程序.
结论:
- 在道路安全方面,YOLOv8提供了一个高度准确和高效的解决方案,用于自动检测视线区域.
- 该系统的准确性取决于反映特定驾驶员和条件的培训数据.
- 需要进一步的研究来评估在各种现实驾驶场景中的性能.
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