Defect detectability analysis via probability of defect detection between traditional and deep learning methods in numerical simulations

Keyphrases

• 2D Image 14%
• 3D Image 14%
• 3D U-Net 14%
• Aluminum Alloy Cylinder Head 14%
• Challenging Problems 14%
• Computed Tomography Data 14%
• Computed Tomography Simulation 28%
• Deep Learning 100%
• Defect Detectability 100%
• Defect Detection 100%
• Defect Probability 100%
• Defect Types 14%
• Detectability Analysis 100%
• Detection Curve 28%
• Detection Efficiency 14%
• Detection Method 14%
• Detection Performance 14%
• Fiber Cracking 14%
• Image Detection 14%
• Image Quality 14%
• Imaging Techniques 14%
• K-means 14%
• Limit of Detection 28%
• Non-destructive Testing 14%
• Otsu Thresholding 14%
• Quality Performance 14%
• Radiograph 14%
• Segmentation Algorithm 14%
• Segmentation Method 14%
• Tomography, X-ray Computed 57%
• Traditional Segmentation 14%
• Traditional Teaching Method 100%
• U-Net 14%
• V-Net 14%
• Well-defined 14%
• X-ray 14%
• X-ray Computed Tomography Images 28%
• X-ray Images 14%

Material Science

• Cylinder 16%
• Non-Destructive Testing 16%
• X-Ray Computed Tomography 100%