Infrared image identification method of substation equipment fault under
  weak supervision
Anjali Sharma Priya Banerjee Nikhil Singh
This study presents a weakly supervised method for identifying faults in
infrared images of substation equipment. It utilizes the Faster RCNN model for
equipment identification, enhancing detection accuracy through modifications to
the model's network structure and parameters. The method is exemplified through
the analysis of infrared images captured by inspection robots at substations.
Performance is validated against manually marked results, demonstrating that
the proposed algorithm significantly enhances the accuracy of fault
identification across various equipment types.