Interpretable Computer Vision for Defect Detection in X-ray Tomography of Aerospace SiC/SiC Composites
Researchers have developed a new interpretable deep learning model, p-ResNet-50, for detecting defects in aerospace composites using X-ray tomography. This model not only achieves high accuracy comparable to traditional black-box networks but also provides case-based explanations by aligning learned prototypes with expert-defined defect categories. The framework enhances traceability for inspection decisions and explicitly maps regions of uncertainty, making it suitable for industrial applications requiring auditable outcomes. AI
IMPACT Introduces a novel interpretable AI methodology for industrial defect detection, enhancing traceability and audibility in critical applications.