Mamba-based framework enhances XCT defect classification with self-supervision

Researchers have developed NL-MambaXCT, a novel framework utilizing Mamba architecture and self-supervised learning for defect classification in X-ray computed tomography (XCT) images of Nomex honeycomb structures. This approach combines masked image modeling for pre-training on unlabeled data with a Nested Learning formulation, featuring two-timescale parameter dynamics and a deep-momentum optimizer. The model achieved high accuracy and F1 scores, outperforming existing CNN, attention, and single-timescale Mamba baselines, suggesting its potential for efficient and robust industrial inspection in aerospace manufacturing. AI

IMPACT This research offers a more efficient and accurate method for defect detection in critical aerospace components, potentially improving manufacturing quality and safety.

RANK_REASON This is a research paper detailing a novel model architecture and methodology for a specific technical problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• X-ray computed tomography
• Mamba
• Nested Learning
• NL-MambaXCT
• Nomex honeycomb
• self-supervised learning

• paper
• model release

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