Machine Learning-based Transformer Station Construction Safety Detection System

The construction safety of transformer stations is an important issue in the power industry, and traditional safety inspection methods suffer from high labor costs and low efficiency. The construction safety detection system for transformer stations based on Convolutional Neural Network (CNN) has attracted widespread attention in recent years, but there is still relatively little comprehensive summary of its research methods and results. This study elaborates on the construction of a CNN-based transformer station construction safety detection system and conducts a deep comparison with traditional SVM models. Through the analysis of research literature, it was found that CNN can effectively distinguish between safe and dangerous behaviors by learning image features and contextual relationships, improving the accuracy of detection. The parallel computing and GPU acceleration capabilities of CNN significantly shorten the detection time and improve the real-time performance of detection.