Unsupervised Insulator Defect Detection Method Based on Masked Autoencoder

With the rapid expansion of high-speed rail infrastructure, maintaining the structural integrity of insulators is critical to operational safety. However, conventional defect detection techniques typically rely on extensive labeled datasets, struggle with class imbalance, and often fail to capture l...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 25; no. 14; p. 4271
Main Authors:	Song, Yanying, Xiong, Wei
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 09.07.2025 MDPI
Subjects:	Accuracy Analysis Annotations Datasets Deep learning Defects Ethical aspects insulator defect detection interval masking inference Localization masked autoencoder (MAE) Methods Neural networks Passenger rail services random masking training Semantics unsupervised learning vision transformer (ViT) interval masking inference insulator defect detection vision transformer (ViT) masked autoencoder (MAE) random masking training defect detection and localization unsupervised learning
ISSN:	1424-8220, 1424-8220
Online Access:	Get full text
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