Drowsiness on the road is a widespread problem with fatal consequences; thus, a multitude of systems and techniques have been proposed. Among existing methods, Ghoddoosian et al. utilized temporal blinking patterns to detect early signs of drowsiness, but their algorithm was tested only on a powerful desktop computer, which is not practical to apply in a moving vehicle setting. In this paper, we propose an efficient platform to run Ghoddosian's algorithm, detail the performance tests we ran to determine this platform, and explain our threshold optimization logic. After considering the Jetson Nano and Beelink (Mini PC), we concluded that the Mini PC is the most efficient and practical to run our embedded system in a vehicle. To determine this, we ran communication speed tests and evaluated total processing times for inference operations. Based on our experiments, the average total processing time to run the drowsiness detection model was 94.27 ms for Jetson Nano and 22.73 ms for the Beelink (Mini PC). Considering the portability and power efficiency of each device, along with the processing time results, the Beelink (Mini PC) was determined to be most suitable. Also, we propose a threshold optimization algorithm, which determines whether the driver is drowsy or alert based on the trade-off between the sensitivity and specificity of the drowsiness detection model. Our study will serve as a crucial next step for drowsiness detection research and its application in vehicles. Through our experiment, we have determinend a favorable platform that can run drowsiness detection algorithms in real-time and can be used as a foundation to further advance drowsiness detection research. In doing so, we have bridged the gap between an existing embedded system and its actual implementation in vehicles to bring drowsiness technology a step closer to prevalent real-life implementation.
翻译:路边的沉睡是一个广泛且具有致命后果的问题; 因此, 提出了众多的系统和技术。 在现有的方法中, Ghoddoosian 等人( Ghoddoosian et al.) 使用了时间闪烁模式来检测早期沉睡迹象, 但是他们的算法只是在一个强大的台式计算机上测试, 这在移动车辆设置中是不切实际的。 在本文中, 我们提议一个高效的平台来运行Ghoddosian 的算法, 详细描述我们为确定这个平台而运行的性能测试, 并解释我们的实际优化逻辑。 在考虑Jetson Nano和Beelink (Mini PC) (Jetson Nano) 和 Beelink (Mini PC) 之后, 我们的结论是, Mini PC (Mini PC) 使用时间闪烁闪烁模式来运行我们的嵌嵌嵌入系统。 根据我们的现有测算, 将运行一个更精确的触摸测算系统 确定一个更精确的解算法 。