论文标题
通过强大的深度学习模型评估驾驶员疲劳
Towards Evaluating Driver Fatigue with Robust Deep Learning Models
论文作者
论文摘要
在本文中，我们探索了不同的基于深度学习的方法来检测驾驶员疲劳。昏昏欲睡的驾驶会导致美国每年大约72,000次撞车事故和44,000人受伤，并发现昏昏欲睡并提醒驾驶员可以挽救许多生命。有许多检测疲劳的方法，其中一种眼睛的闭合度检测是一种。我们提出了一个框架，以检测被捕获的相机框架中的眼睛闭合度，作为检测嗜睡的网关。我们探索两个不同的数据集，以检测眼睛的闭合度。我们通过使用野外闭合眼（CEW）的闭合眼睛使用新的眼光数据集和面部模型来开发眼睛模型。我们还探索了不同的技术，可以通过添加噪声来使模型更强大。我们在眼睛模型上获得了95.84％的精度，而面部模型的精度为80.01％。我们还看到，通过对抗性培训和数据增强，我们可以将面部模型的准确性提高6％。我们希望我们的工作对驾驶员疲劳检测领域有用，以避免与昏昏欲睡有关的潜在车辆事故。
In this paper, we explore different deep learning based approaches to detect driver fatigue. Drowsy driving results in approximately 72,000 crashes and 44,000 injuries every year in the US and detecting drowsiness and alerting the driver can save many lives. There have been many approaches to detect fatigue, of which eye closedness detection is one. We propose a framework to detect eye closedness in a captured camera frame as a gateway for detecting drowsiness. We explore two different datasets to detect eye closedness. We develop an eye model by using new Eye-blink dataset and a face model by using the Closed Eyes in the Wild (CEW). We also explore different techniques to make the models more robust by adding noise. We achieve 95.84% accuracy on our eye model and 80.01% accuracy on our face model. We also see that we can improve our accuracy on the face model by 6% via adversarial training and data augmentation. We hope that our work will be useful to the field of driver fatigue detection to avoid potential vehicle accidents related to drowsy driving.
