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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Vydáno v:	Sensors (Basel, Switzerland) Ročník 25; číslo 14; s. 4271
Hlavní autoři:	Song, Yanying, Xiong, Wei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 09.07.2025 MDPI
Témata:	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
On-line přístup:	Získat plný text
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