Research on Sleep-deprived driving Recognition Technology Based on Improved YOLOv5
Sleep-deprived driving is one of the main causes of traffic accidents,and it is also the research focus of major automobile manufacturers in the field of intelligent and safe driving.Due to the discrete and sparse temporal characteristics of the prominent facial fatigue features of drivers,a study proposes to extract shallow features from the backbone network using the YOLOv5 algorithm and perform multi feature fusion.A shallow feature prediction head is added to form a multi prediction head detection layer,and a lightweight ECA attention module is integrated into the Neck feature enhancement network to jointly form an improved M-YOLOXs Sleep-deprived driving detection algorithm,Through experimental analysis and statistical analysis,the improved algorithm has improved the detection accuracy by 2.68%compared to the original YOLOv5 framework,effectively improving the recognition accuracy of driver fatigue status.
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