Image Quality Intelligent Detection Method on Electric Power Video Surveillance

At present, artificial intelligence technology in power has achieved certain application results in the field of equipment defect recognition and perception.This article proposes a joint self-supervised learning method based on contrastive learning and sorting learning, and applies it to pre-training of power inspection image quality feature extraction models. By effectively utilizing massive unlabeled image samples, the training of feature extraction models is achieved, effectively improving the model’s perception of image quality deterioration and scene generalization ability; Then, based on the pre-training model of joint learning, further supervised fine-tuning is carried out, and the results of general data pre-training, contrastive learning, and joint learning test sets are compared through experiments to verify the effectiveness of joint learning in intelligent detection of power image quality deterioration.