Research on image classification of power inspection using less sample learning technique

Abstract

Abstract This study proposes a few-sample self-supervised power inspection image classification method based on Vision Transformer and masked autoencoders (MAE). Initially, an MAE is employed to pretrain an extensive collection of unlabeled power inspection images, leveraging the masking technique to encourage the model to acquire an understanding of the overarching features within these images. Then, the pretrained encoder part is combined with the visual transformer to further extract image features. For the subsequent classification task, the encoder’s pretrained weights are frozen and subsequently incorporated into a novel classification network. By fine-tuning this network with a limited amount of labeled data, experimental findings indicate a substantial enhancement in the classification accuracy of power inspection images, particularly in scenarios with limited sample sizes. By introducing visual transformers and MAE, this article provides an efficient and reliable solution for intelligent classification of power inspection images, which has important practical application value and broad market prospects.