C. Fisher

Real-Time PAUT Defect Classification with Class-Weighted Knowledge Distillation

Minsu Jeon, R. Guyon, C. Fisher, D. Mun, Jaebeom Lee

TL;DR: This work proposes a PAUT defect-classification framework that minimizes false negatives by applying class-weighted Knowledge distillation (KD) to transfer a Vision Transformer (ViT) teacher’s diagnostic capability to an extremely lightweight linear student.

Phased array ultrasonic testing (PAUT) provides high-resolution subsurface imaging through electronic beam steering and focusing and is widely used for internal defect diagnosis in structures. However, effectively interpreting PAUT data requires understanding complex spatial-temp…Read more