基于SSD的多因素融合的驾驶疲劳检测研究

为了降低因疲劳驾驶而导致的事故发生率，提出一种利用卷积神经网络与人脸特征点、疲劳判定指标相融合的方法，共同构建疲劳驾驶检测模型。首先利用SSD（Single Shot MultiBox Detector）网络定位驾驶员的眼睛与嘴巴区域，VGG16 网络学习这两个区域所包含的疲劳特征；同时再结合人脸68特征点、眼睛纵横比（Eye Aspect Ratio, EAR）和嘴巴纵横比（Mouth Aspect Ratio, MAR）共同判定驾驶疲劳状态。最后，在相同测试集下分别计算SSD算法和Faster-RCNN算法的平均精度均值mAP；在YawDD数据集上应用此模型；并通过模拟驾车环境来验证此模型的可行性。实验结果表明，SSD算法要优于Faster-RCNN算法，并且此模型在YawDD数据集上的检测准确率约达97.2%，摄像头也能对驾驶员的状态进行实时检测。此模型对疲劳状态的检测十分有效，可在一定程度上降低因疲劳驾驶而导致的事故发生率。

Research on driving fatigue detection based on SSD muti-factor fusion

In order to reduce the incidence of accidents caused by fatigue driving, a method is proposed to build a fatigue driving detection model by integrating convolutional neural network with face feature points and fatigue indicators. Firstly, the driver''s eyes and mouth areas are located by the SSD network, and the VGG16 network learns the fatigue features contained in the eye and mouth areas. At the same time, 68 feature points of face, eye aspect ratio and mouth aspect ratio are combined to determine the driving fatigue state. Finally, the mean average precision of SSD algorithm and Faster-RCNN algorithm is calculated under the same test set. The model is applied to YawDD dataset. And the feasibility of this model is verified by simulating driving environment. The experimental results show that SSD algorithm is better than Faster-RCNN algorithm, the detection accuracy of this model on YawDD dataset is about 97.2%, and the camera can also detect the driver''s state in real-time. The model is effective in detecting fatigue state and reducing the accident rate caused by fatigue driving to a certain extent.