A Method for Identifying High-altitude Falling Hazard Behavior Based on Deep Learning
Deep Learning algorithms represented by Convolutional Neural Networks can extract human behavior features more accurately and effectively,applying Deep Learning to human behavior recognition and prediction has become a research hotspot.On the basis of the classic HRnet network structure,this paper proposes a new network model L-HRnet by improving the L-Swish activation function and introducing the Squeeze-and-Excitation module,which is used to determine whether the behavioral actions of construction worker during high-altitude operations are dangerous.Behavioral classification and recognition experiments are conducted on the public dataset HMDB51,and the results show that the improved network structure L-HRnet had significantly better recognition accuracy than HRnet,effectively improving the protection level of high-altitude workers.
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