用摄像头实时检测司机困倦，通过眨眼频率和眼睑闭合度预警。
A Real-Time Driver Drowsiness Detection System Using MediaPipe and Eye Aspect Ratio
- 基于MediaPipe的面部关键点追踪，用眼宽比（EAR）判断困倦状态。
- 检测到长时间闭眼或眨眼过少时立即发出声音警报，响应速度快。
- 低成本高效率，适合集成进现有智能驾驶辅助系统。
道路交通事故的主要原因之一是司机疲劳，每年造成数千起伤亡。本研究开发了一套驾驶员困倦检测系统，旨在通过实时提醒困倦司机提升行车安全。系统利用标准网络摄像头，借助MediaPipe的Face Mesh框架精准高效地追踪面部特征，重点分析眼部运动。通过眼宽比（EAR）方法监测闭眼时间与眨眼频率，当检测到持续闭眼或眨眼过少等困倦迹象时，系统会触发声音警报以唤醒驾驶员。该系统结合OpenCV进行图像处理、MediaPipe实现人脸识别，在测试中表现出高准确率与快速响应能力，验证了其作为当前高级驾驶辅助系统（ADAS）组成部分的可行性。
原文摘要 · Abstract (English)
One of the major causes of road accidents is driver fatigue that causes thousands of fatalities and injuries every year. This study shows development of a Driver Drowsiness Detection System meant to improve the safety of the road by alerting drivers who are showing signs of being drowsy. The system is based on a standard webcam that tracks the facial features of the driver with the main emphasis on the examination of eye movements that can be conducted with the help of the Eye Aspect Ratio (EAR) method. The Face Mesh by MediaPipe is a lightweight framework that can identify facial landmarks with high accuracy and efficiency, which is considered to be important in real time use. The system detects the moments of long eye shutdowns or a very low rate of blinking which are manifestations of drowsiness and alerts the driver through sound to get her attention back. This system achieves a high-performance and low-cost driver monitoring solution with the help of the computational power of OpenCV to process the image and the MediaPipe to identify faces. Test data experimental analyses indicate that the system is very accurate and responds quicker; this confirms that it can be a component of the current Advanced Driving Assistance System (ADAS).
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