Image Similarity Measurement for The Quality Control of Electricity Substation Inspection

Abstract

The routine inspection of an electricity substation helps to detect faults and repair equipment in time, ensuring the substation to work safely. However, due to irregular operations, some inspectors may miss to capture images at certain spots while take multiple similar images at the same spots. In order to make control of the inspection quality, we design an algorithm to find such situation automatically. Specifically, given two images, we design a registration-based method to evaluate the affine transformation between two images, then we evaluate the averaged corner error by comparing the image transformed with respect to the estimated affine transformation to an identical transformation. Finally, we screen out the similar images that are small in the averaged corner error. These images are very likely to be taken at the same inspection spot. We conduct experiments on a dataset collected during one routine inspection of a whole substation. Experimental results show that our method is effective to screen out similar images, helping to build an automatic quality control process of the routine inspection.