An intelligent recognition method of factory personnel behavior based on deep learning.

• An improved model for recognizing factory personnel's abnormal behavior in complicated situations has been proposed. • Detection accuracy is improved by introducing a backbone network enhanced by Bi-level routing attention. • The robustness of the model is enhanced and the complexity is reduced by the C3 module of the neck network is replaced with the CNeB module. • The mean average precision of the model was improved to 86.7 % and the detection speed reached the standard of real-time detection. The real-time and accurate recognition of abnormal behavior among factory personnel helps enhance their awareness of hazardous environments, thereby reducing the occurrence of accidents. This paper proposes a behavior recognition network based on an attention mechanism and a high-efficiency convolution module. The Bi-Level Routing Attention was introduced to the backbone network, thus enhancing the attention of the recognition network to the target region effectively. The recognition accuracy was further strengthened by improving the neck network based on the ConvNeXt Block module while reducing the model complexity. Thirteen additional recognition models were constructed to enhance the original network from various perspectives. Subsequently, the mean average precision and detection speed of each model were evaluated. Experimental results demonstrated that the detection accuracy of the target recognition network proposed in this paper has been significantly improved, the detection speed meets the real-time requirements, and the comprehensive performance is the most superior compared with other diverse and improved networks. The proposed recognition model can accurately identify a variety of factory personnel's abnormal behaviors in real-time, and it has practical application significance for the problem of personnel safety identification in the factory. [ABSTRACT FROM AUTHOR]