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基于YOLOv8的单阶段车灯状态检测
1Graduate School of Automotive Engineering, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul 02707, Republic of Korea.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
本研究引入了一种新的方法,用于使用YOLOv8检测车辆车灯状态,以提高先进驾驶辅助系统 (ADAS) 和自动驾驶的安全性和可解释性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 汽车工程 汽车工程
背景情况:
- 先进的驾驶辅助系统 (ADAS) 和自动驾驶在实现3级自动化方面面临着挑战.
- 3级自动化要求提高安全性和可解释性,需要可靠的车辆提示检测.
- 车灯状态是人类驾驶员和自动驾驶系统的关键视觉提示.
研究的目的:
- 开发一种用于检测驾驶车辆及其车灯状态的新方法.
- 提高先进驾驶辅助系统 (ADAS) 和自动驾驶技术的安全性,可解释性和舒适性.
- 为车灯状态检测研究提供公开可用的数据集.
主要方法:
- 开发了一个基于YOLOv8的快速和准确的单阶段车灯状态检测网络.
- 转移学习使用一个自定义的数据集,包括超过11000个前向图像和手动注释.
- 该方法在边缘设备 (Jetson Nano) 上评估了检测准确度和推断时间.
主要成果:
- 拟议的YOLOv8网络准确地检测车辆及其车灯状态.
- 通过0.766和0.793.3之间的平均平均精度 (mAP50) 实现了高检测性能.
- 在Jetson Nano边缘设备上记录了133.30毫秒的短推理时间.
结论:
- 拟议的方法实现了高精度和快速推断车灯状态检测.
- 这为ADAS和自动驾驶提供了有价值的输入信息,提高了安全性和可解释性.
- 公开可用的数据集支持在汽车技术这一关键领域的进一步研究.
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