用深度学习实时检测司机困倦，准确率超99%。
Real-Time Drivers' Drowsiness Detection and Analysis through Deep Learning
- 通过摄像头捕捉面部特征，用DCNN分析眼开度和打哈欠动作。
- 在NTHU-DDD数据集上准确率达99.6%，Yawn-Eye-Dataset上达97%。
- 非侵入式低成本方案，适合智能车载系统部署。
长途驾驶易引发司机疲劳，危及自身与他人安全，亟需实时预警系统。本文基于深度卷积神经网络（DCNN）与OpenCV，构建实时司机困倦检测模型。系统通过实时摄像头采集驾驶员面部图像，利用Python库OpenCV提取眼开度、打哈欠等面部特征，再由预训练的DCNN模型判断困倦状态。一旦识别为困倦，系统将立即触发持续警报，嵌入智能汽车技术中。该方法无需穿戴设备，成本低且无侵入性。实验表明，该模型在NTHU-DDD数据集上达到99.6%的分类准确率，在Yawn-Eye-Dataset上达到97%的准确率，具备实际应用潜力。
原文摘要 · Abstract (English)
A long road trip is fun for drivers. However, a long drive for days can be tedious for a driver to accommodate stringent deadlines to reach distant destinations. Such a scenario forces drivers to drive extra miles, utilizing extra hours daily without sufficient rest and breaks. Once a driver undergoes such a scenario, it occasionally triggers drowsiness during driving. Drowsiness in driving can be life-threatening to any individual and can affect other drivers' safety; therefore, a real-time detection system is needed. To identify fatigued facial characteristics in drivers and trigger the alarm immediately, this research develops a real-time driver drowsiness detection system utilizing deep convolutional neural networks (DCNNs) and OpenCV.Our proposed and implemented model takes real- time facial images of a driver using a live camera and utilizes a Python-based library named OpenCV to examine the facial images for facial landmarks like sufficient eye openings and yawn-like mouth movements. The DCNNs framework then gathers the data and utilizes a per-trained model to detect the drowsiness of a driver using facial landmarks. If the driver is identified as drowsy, the system issues a continuous alert in real time, embedded in the Smart Car technology.By potentially saving innocent lives on the roadways, the proposed technique offers a non-invasive, inexpensive, and cost-effective way to identify drowsiness. Our proposed and implemented DCNNs embedded drowsiness detection model successfully react with NTHU-DDD dataset and Yawn-Eye-Dataset with drowsiness detection classification accuracy of 99.6% and 97% respectively.
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