Browsing Faculty of Engineering & Applied Science by Subject "Defect detection"
Now showing items 1-3 of 3
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Deep learning methods applied to anomaly detection in vehicle manufacturing and operations
(2019-09-01)As one of the most common modes of transportation, vehicles are very closely related to our lives. As a result, safety is an important issue in both vehicle production process and vehicle operations. Recently, unmanned ... -
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, ... -
Defect detection using 3D computed tomography images and application on nuclear power plants
(2022-12-01)Tools used in nuclear power plant (NPP) inspection are required to be inspected before and after use on a reactor to check their integrity. To address the long duration required for manual inspection, non-destructive testing ...