Research on Fatigue Driving Detection Method Based on Deep Learning
Fatigue driving detection is very important to avoid the occurrence of vehicle accidents,and has high requirements for the real-time and accuracy of detection methods.To this end,a method of fatigue driving detection based on deep learning is pro-posed.First,the improved target detection network YOLOX is used to locate the driver's facial area,and then the PFLD deep learn-ing model is used to detect key points of the face,thereby calculating feature parameters such as blinking frequency,yawning fre-quency,and nodding frequency.Finally,a multi-feature fusion fatigue determination algorithm is used to determine the driver's fa-tigue state,so as to provide effective early warning of fatigue driving.A large number of experiments show that this method has achieved significant performance improvements in the real-time and accuracy of fatigue driving detection.
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