# Unsupervised Insulator Defect Detection Method Based on Masked Autoencoder

## TL;DR

This paper introduces a new unsupervised method for detecting defects in insulators using a masked autoencoder, improving efficiency and accuracy without needing labeled data.

## Contribution

A novel unsupervised defect detection framework using a masked autoencoder with dual-pass masking for improved localization.

## Key findings

- The method achieves competitive image- and pixel-level performance on benchmark datasets.
- It significantly reduces computational overhead compared to existing ViT-based approaches.
- The dual-pass interval masking strategy enhances defect localization accuracy.

## Abstract

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.