RobustDefect-LLM: Explainable and Robustness-Aware Industrial Surface Defect Classification with Decision Support and AI-Assisted Reporting
This paper presents RobustDefect-LLM, an industrial surface-defect inspection framework integrating deep-learning classification, operator-facing visual evidence, confidence-aware decision support, controlled AI-assisted reporting, traceable storage, and mobile interaction in a unified quality-control workflow. Here, robustness-aware denotes explicit evaluation under controlled image degradation and confidence-aware review routing, not an intrinsic robustness guarantee. Four transfer-learning-based convolutional neural networks, ResNet50, EfficientNet-B0, DenseNet121, and MobileNetV3-Large, were evaluated on 1,799 images from the six-class NEU-DET dataset using fixed training, validation,...
| Authors | Nazlıcan Düşünmez, Halûk Gümüşkaya |
|---|---|
| Primary category | cs.CV |
| Published | 9 August 2026 |
| Last updated at source | 9 August 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.