Improved fatigue driving detection method based on attention mechanism
Due to the poor recognition angle and partial area occlusion in the process of fatigue driving acquisition,the time information with different characteristics is lost in different time periods,resulting in poor universality of the algorithm.In addition,the detection of driving fatigue needs to not only ensure the comprehensive accuracy,but also have a lower missed detection rate.To solve the above problems,a fatigue driving detection model based on attention mechanism and long short-term memory(LSTM)neural network is proposed.By calculating multi-dimensional feature vectors for different feature localization points and learning the time series of feature vectors,the attention mechanism is introduced to give different probability weights to the hidden states of each dimension,so as to strengthen the influence of important information on the determination of fatigue state and reduce the influence of historical data losing feature information on parameters.According to the experiment,this method has accuracy of 92.19％and missed detection rate of 1.9％in more general detection environment,and the missed detection rate is only 3.07％in the environment where some features are lost.
万方数据
维普
