Weakly-supervised Infrared Image Recognition Method for Substation Equipment Fault Detection

To address the recognition problem of infrared images for substation equipment faults, a weakly supervised recognition method is proposed. First, based on the faster region convolutional neural network(RCNN) model, substation equipment is identified, and the accuracy of equipment detection is further improved by adjusting the model's network structure and parameters. Second, considering the inherent physical characteristics of various operational equipment in substations, an image feature extraction method is proposed based on kernel density estimation of temperature probability distributions. Finally, to tackle challenges such as difficulties in data collection in practical applications, weakly supervised learning is introduced. Labeled data are used to compute prototype vectors…