自动驾驶汽车的车道和交通标志检测:解决印度道路状况的挑战
H S Gowri Yaamini1, Swathi K J1, Manohar N1
1Department of Computer Science, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Mysuru 570026, India.
MethodsX
|February 6, 2025
概括
这项研究通过使用YOLOv8纳米转移学习改进车道和交通标志检测,提高了印度的自动驾驶汽车安全性. 这些模型实现了高精度和速度,这对于各种印度道路条件至关重要.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 自动驾驶汽车需要准确的车道和交通标志检测才能安全运行.
- 由于数据集的局限性,现有的模型经常在复杂的印度交通条件下失败.
- YOLOv8纳米变体提供了精度和计算效率的平衡.
研究的目的:
- 开发和评估YOLOv8n转移学习模型,用于在印度强大的车道和交通标志检测.
- 为了应对印度各种道路和天气条件所带来的具体挑战.
- 为了提高在印度运行的自动驾驶汽车的性能.
主要方法:
- 使用了YOLOv8n转移学习方法,并进行了架构调整.
- 训练模型在一个多样化的数据集上的22,400图像捕捉复杂的印度场景.
- 基于平均平均精度 (mAP) 和推断速度 (fps) 的评估性能.
主要成果:
- 实现了90.6%的mAP和117fps的车道检测.
- 在交通标志检测方面达到81.3%的mAP和56fps.
- 在城市,郊区和高速公路场景中表现出卓越的性能.
结论:
- 拟议的YOLOv8n转移学习模型显示了印度交通条件的显著改善.
- 该模型有效地处理各种场景,包括封闭的车道和受天气影响的标志.
- 这种方法提高了印度自动驾驶汽车的安全性和效率.
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