通过整合轻量级YOLOv5s和面部3D关键点来研究疲劳驾驶检测方法
Xiansheng Ran1, Shuai He1, Rui Li1
1School of Mechatronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
本研究介绍了一种轻量级的实时疲劳驾驶检测模型,使用改进的YOLOv5s和注意力网3D关键点提取. 该模型显著降低了计算负载和参数,同时实现了96.3%的疲劳识别率,以实现更安全的驾驶.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 汽车安全 汽车安全
背景情况:
- 疲劳驾驶带来了重大安全风险,需要有效的检测方法.
- 现有的模型经常受到高计算需求和不充分的面部特征提取的影响.
- 轻量化和实时解决方案对于车辆的实际部署至关重要.
研究的目的:
- 开发一种轻量级的实时疲劳驾驶检测模型.
- 为了提高面部特征关键点提取精度和效率.
- 为了减少现有模型的计算和参数要求.
主要方法:
- 使用Shufflenetv2_BD重建骨干网络,以减少参数复杂性.
- 集成了改进的交叉规模聚合模块 (CAM),以最大限度地减少浅层特征信息丢失.
- 开发一个环境信息融合模块,将高效多尺度模块 (EAM) 和深度过度参数化卷积 (DoConv) 结合起来.
- 通过Wise-IoU (WIoU) 重新定义损失函数,以实现更快的模型融合.
- 通过将检测结果与关闭眼睛,打哈欠和PERCLOS (随着时间的推移,眼关闭对瞳孔的百分比) 的值结合起来,构建最终模型.
主要成果:
- 与基线相比,参数减少了58%,模型大小减少了56.3%.
- 将计算负载降低到5.9 GFLOPs.
- 与基线模型相比,平均准确度提高了1%.
- 达到96.3%的疲劳识别率.
结论:
- 拟议的算法可实现准确和稳定的实时疲劳驾驶检测.
- 该模型具有轻量级,可显著降低计算负载和参数.
- 在车辆终端中为轻量部署疲劳驾驶检测系统提供了强有力的支持.
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