Meter image enhancement method in high light substation based on improved CycleGAN

Abstract

When the patrol robot recognizes the instrument under strong light, the collected instrument image has serious exposure, details loss and other phenomena. Aiming at the above problems, an improved CycleGAN method for enhancing the instrument image in the substation under strong light is proposed. First of all, for the generator, the CSD network and the self-attention mechanism ACmix module are improved to improve the lighting processing effect. Secondly, the idea of Patch-GAN is adopted in the discriminator, and the feature graph is mapped to an N×N matrix at the end to improve the processing ability of the details. The experimental results show that the peak signal-to-noise ratio of the image is increased from 15dB to 20dB after the above improvement; The index of structural similarity increased from 0.5 to 0.8; The recognition accuracy after enhancement has also been improved by 6%. In conclusion, the method in this paper can eliminate the influence of illumination on instrument recognition, and has strong robustness.