An Eye Fatigue Recognition System using YOLOv2

Chuanhui Lau, Hungyang Leong, Joonhuang Chuah, N. Kamarudin
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Abstract

The rapid increase in global population significantly drives the hiking demand for transportations. This trend further leads to the increase in the number of road traffic accidents globally. Based on a study, fatigue due to prolonged driving is one of the leading causes for traffic accidents. With a customized Graphical User Interface (GUI), this work aims to develop an eye fatigue recognition system using YOLOv2 model. The proposed method used PERCLOS and blink rate parameters as indicators to determine the alertness of the user. This proposed method achieved a real-time average accuracy of 99.23% in normal lighting conditions and 98.57% in low light conditions.
基于YOLOv2的眼疲劳识别系统
全球人口的快速增长极大地推动了对交通工具的徒步旅行需求。这一趋势进一步导致全球道路交通事故数量的增加。根据一项研究,长时间驾驶引起的疲劳是导致交通事故的主要原因之一。通过定制化的图形用户界面(GUI),本工作旨在开发一个使用YOLOv2模型的眼疲劳识别系统。该方法采用PERCLOS和眨眼频率参数作为判断用户警觉性的指标。该方法在正常光照条件下的实时平均精度为99.23%,在弱光照条件下的实时平均精度为98.57%。
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