A method for transformer substation personnel behavior recognition based on improved AlphaPose algorithm

In recent years, the safety and efficiency of transformer substation operations have gained significant attention. Monitoring personnel behavior within these facilities is crucial for maintaining safety standards and optimizing performance. This paper presents a novel approach to personnel behavior recognition in transformer substations using an improved AlphaPose algorithm. By enhancing the AlphaPose algorithm with additional preprocessing steps, optimized model parameters, and integration with machine learning classifiers, our method achieves superior accuracy and robustness in recognizing various personnel activities. Experimental results demonstrate the effectiveness of the proposed method in real-world transformer substation environments.