Browsing Faculty of Engineering & Applied Science by Subject "Defect classification"
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Deep learning models for defect and anomaly detection on industrial surfaces
(2023-12-01)Automated quality control is essential across various industries to reduce manual inspection and improve operational efficiency. While there are advances in computer vision and machine learning for defect detection, ...