Driver drowsiness is one of the major causes of accidents and fatal road crashes, causing a high human and economic cost. Recently, automatic drowsiness detection has begun to be recognized as a promising solution, receiving growing attention from industry and academics. In this letter, we propose to embed a convolutional neural network (CNN)-based solution in smart connected glasses to detect eye blinks and use them to estimate the driver’s drowsiness level. This innovative solution is compared with a more traditional method, based on a detection threshold mechanism. The performance, battery lifetime, and memory footprint of both solutions are assessed for embedded implementation in the connected glasses. The results demonstrate that CNN outperforms the accuracy obtained by the threshold-based algorithm by more than 7%. Moreover, increased overheads in terms of memory and battery lifetime are acceptable, thus making CNN a viable solution for drowsiness detection in wearable devices.

中文翻译：

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在边缘实现眨眼检测的深度学习

驾驶员困倦是事故和致命道路交通事故的主要原因之一，会造成高昂的人力和经济成本。最近，自动睡意检测开始被认为是一种很有前途的解决方案，越来越受到工业界和学术界的关注。在这封信中，我们建议在智能联网眼镜中嵌入基于卷积神经网络 (CNN) 的解决方案，以检测眨眼并使用它们来估计驾驶员的困倦程度。将此创新解决方案与基于检测阈值机制的更传统方法进行比较。两种解决方案的性能、电池寿命和内存占用都经过评估，以用于联网眼镜中的嵌入式实施。结果表明，CNN 比基于阈值的算法获得的准确率高 7% 以上。而且，