Improved Multi-task Cascaded Convolutional Networks for Driver Fatigue
Aiming at the limitations of single-feature detection in driver fatigue detection methods and the time-consuming detec-tion on low-computing-power mobile edge computing devices due to the excessive computation of model parameters,an improved multi-task cascaded convolutional neural network(MTCNN)was proposed.Through the optimization of the sub-network R-Net,the average pooling was used to reduce the number of model parameters,and the fully-connected layer was replaced by mean pool-ing,combined with the accurate localization of the 64 feature points of the face by Dlib,and the threshold parameter with better ef-fect was selected to achieve fatigue detection.The experimental results show that on the face dataset WIDER FACE and LFW data-set,the improved algorithm reduces the amount of parameters by 47.5％compared to the pre-improvement one,and the accuracy of face detection increases from 96.7％to 97.8％.Finally,through the YawDD fatigue dataset,efficient fatigue detection is realized on the resource-constrained Raspberry Pi 4B device,which verifies its reliability in practical applications.
万方数据
维普
