One of the biggest causes of death in Indonesia is traffic accidents. Driving a vehicle with drowsiness is a dangerous condition and results in an accident. Several previous researches on the detection of drowsiness was applied to determine the trends of night workers and adaptation to night shifts in hospital staff. It is reported the importance of application for drowsiness detection as an effort to improve work safety (1). Then a lot of research on drowsiness have been turned around for the detection of drowsiness for drivers with a variety of methods. Research on drowsiness uses image processing with eyetracking methods, including blink frequency, and PERCLOS, which are used to confirm results (2). Then another study to detect drowsiness by using the neural network method and Viola-Jones to detect facial characteristics (3). Besides, the condition of drivers who are drowsy or alert based on images taken during driving is conducted by analyzing the state of the driver's eyes: opened, half-open, and closed using image processing and ANN (4). Image processing using the human visual system model was used, and changes in energy levels in the frame were utilized (5). Other researches were development the drowsiness detection using the parameters of the Abstract Background: Drowsiness condition is one of the significant factors often encountered when an accident occurs. We aimed to detect a method to prevent accidents caused by drowsiness and lost a focused driver. Methods: The image processing technique has been capable of detecting the characteristic of drowsiness and lost focus driver in real-time using Raspberry Pi. Video samples were processed using the Haar Cascade Classifier method to identify areas of the face, eyes, and mouth so that drowsy conditions. The methods can be determined based on the bject detected. Results: Two parameters were determined, the lost focused and drowsiness driver. The highest accuracy value for driver lost focused detection was 88.00%, while the highest accuracy value for drowsiness driver detection was 90.40%. Conclusion: In general, a system developed with image processing methods has been able to monitor the drowsiness and lost focused drivers with high accuracy. This system still needs improvements to increase performance.

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使用 Raspberry Pi 的困倦和失去注意力的驾驶员监控系统

印度尼西亚最大的死亡原因之一是交通事故。嗜睡驾驶车辆是一种危险情况，会导致事故。先前关于嗜睡检测的几项研究被应用于确定夜班工作人员的趋势和对医院工作人员夜班的适应。据报道，应用嗜睡检测作为提高工作安全的努力的重要性 (1)。于是，围绕着睡意的大量研究，转而采用多种方法检测驾驶员的睡意。嗜睡研究使用眼动追踪方法进行图像处理，包括眨眼频率和 PERCLOS，用于确认结果 (2)。然后另一项研究通过使用神经网络方法和 Viola-Jones 检测面部特征来检测睡意（3）。除了，通过使用图像处理和人工神经网络（4）分析驾驶员眼睛的状态：睁开、半开和闭上，根据驾驶过程中拍摄的图像来判断驾驶员是否昏昏欲睡或警觉。使用了使用人类视觉系统模型的图像处理，并利用了帧中能量水平的变化 (5)。其他研究正在开发使用抽象背景参数的困倦检测：困倦状况是事故发生时经常遇到的重要因素之一。我们的目标是检测一种方法，以防止因困倦和失去专注的驾驶员而造成的事故。方法：图像处理技术已经能够使用树莓派实时检测困倦和失焦驱动器的特征。使用 Haar 级联分类器方法处理视频样本，以识别面部、眼睛和嘴巴的区域，从而确定昏昏欲睡的情况。可以根据检测到的对象确定方法。结果：确定了两个参数，失去注意力的驱动力和嗜睡驱动力。驾驶员失焦检测的最高精度值为88.00%，而瞌睡驾驶员检测的最高精度值为90.40%。结论：一般而言，采用图像处理方法开发的系统能够以高精度监测困倦和失去注意力的驾驶员。该系统仍需要改进以提高性能。失去专注和嗜睡的司机。驾驶员失焦检测的最高精度值为88.00%，而瞌睡驾驶员检测的最高精度值为90.40%。结论：一般而言，采用图像处理方法开发的系统能够以高精度监测困倦和失去注意力的驾驶员。该系统仍需要改进以提高性能。失去专注和嗜睡的司机。驾驶员失焦检测的最高精度值为88.00%，而瞌睡驾驶员检测的最高精度值为90.40%。结论：一般而言，采用图像处理方法开发的系统能够以高精度监测困倦和失去注意力的驾驶员。该系统仍需要改进以提高性能。