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An Automated Vision-Based Inspection System for Metallic Lock Surface Defects Using a Transformer-Enhanced U-Net

Hong-Dar Lin1, Shun-Yan Li1, Chou-Hsien Lin2

  • 1Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung 413310, Taiwan.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary

This study introduces an advanced visual inspection framework for metallic lock components, overcoming challenges like reflections and low-contrast defects. The system uses controlled imaging and deep learning for reliable surface anomaly detection.

Keywords:
TransU-Netdeep learningindustrial machine visionmetallic surface defect inspectionsemantic segmentation

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