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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Summary: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 large-scale structural anomalies. In this paper, we present an unsupervised insulator defect detection framework based on a masked autoencoder (MAE) architecture. Built upon a vision transformer (ViT), the model employs an asymmetric encoder-decoder structure and leverages a high-ratio random masking scheme during training to facilitate robust representation learning. At inference, a dual-pass interval masking strategy enhances defect localization accuracy. Benchmark experiments across multiple datasets demonstrate that our method delivers competitive image- and pixel-level performance while significantly reducing computational overhead compared to existing ViT-based approaches. By enabling high-precision defect detection through image reconstruction without requiring manual annotations, this approach offers a scalable and efficient solution for real-time industrial inspection under limited supervision.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25144271