Research on Power Safety Monitoring Action Recognition Algorithm Through Neural Network and Deep Learning

Abstract

This paper studied the methods for improving the accuracy and efficiency of the action of power employees by deeply studying the application of neural network algorithm in power safety supervision video. Firstly, this paper summarizes the research status of power safety monitoring system and related action recognition algorithms. For the power industry, timely and accurate identification of workers' movements is essential for accident prevention and management. In this context, various motion recognition algorithms are emerging, among which neural network algorithm has attracted much attention due to its excellent performance in image processing and pattern recognition. Through deep learning, neural networks can automatically learn key features from a large number of video data, providing a more reliable means for human action recognition. Secondly, this paper introduces in detail the proposed method of power safety monitoring video personnel action recognition based on neural network algorithm. Through the optimization of neural network structure and the careful selection of training data, we construct an efficient and accurate action recognition model. The model can quickly and accurately identify the actions of different personnel at work by monitoring the video of electric power operation, including common operation actions and special actions in emergency situations. Our method can more comprehensively understand the various actions of personnel in the electric power environment. Through the analysis of a large number of experimental results, we verify the effectiveness and robustness of the proposed algorithm. Compared with traditional methods, the motion recognition algorithm based on neural network has achieved significant improvement in accuracy and response speed. This proves the practical application prospect of this method in the field of power safety supervision.