RobustDefect-LLM: Explainable and Robustness-Aware Industrial Surface Defect Classification with Decision Support and AI-Assisted Reporting

• URL: http://arxiv.org/abs/2608.08589v1
• Date: Sun, 09 Aug 2026 09:08:13 GMT
• Title: RobustDefect-LLM: Explainable and Robustness-Aware Industrial Surface Defect Classification with Decision Support and AI-Assisted Reporting
• Abstract summary: The RobustDefect-LLM framework integrates deep-learning classification, operator-facing visual evidence, confidence-aware decision support, and controlled AI-assisted reporting.<n>MobileNetV3-Large achieved the highest numerical test accuracy (99.26%) and macro F1-score (0.9926), with a 95% accuracy CI of 0.9815-1.0000.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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, and held-out in-domain test partitions. MobileNetV3-Large achieved the highest numerical test accuracy (99.26%) and macro F1-score (0.9926), with a bootstrap 95% accuracy CI of 0.9815-1.0000. An exact paired McNemar test found no significant difference from DenseNet121 (p = 1.000). The selected model averaged 0.060 s per CPU forward pass (16.66 FPS). Under combined synthetic degradation, accuracy fell to 87.78% at mild intensity and below 40% at stronger intensities, revealing sensitivity to severe image-quality deterioration. Grad-CAM supplied visual evidence, while predictions with confidence below 0.90 or a top-2 margin below 0.10 were routed to HUMAN REVIEW. This conservative policy provided 12.22% automatic coverage and 100% observed selective accuracy among 33 eligible cases (95% CI: 89.43%-100.00%), while routing both observed classification errors to review. Under nominal controlled conditions, all 100 generated reports passed deterministic consistency checks, with a mean latency of 1.66 s. Results support the feasibility of the integrated workflow while emphasizing the need for calibration, repeated evaluation, and real-world industrial validation.

Related papers

• Deep Learning-based Intelligent Diagnosis of Congenital Uterine Anomalies in 3D Ultrasound [10.135095835279495]
  We develop an intelligent framework, termed CUA-Net, for the automated classification of congenital uterine anomalies (CUA)<n> CUA-Net was built on 3D ResNet-18, equipped with a dynamic data resampling strategy to mitigate the data imbalance issue.<n>We compared the CUA-Net with different deep-learning methods and junior/senior sonographers in the testing set.
  arXiv  Detail & Related papers  (2026-09-14T08:44:47Z)
• Pixel-Precise Explainable Stress Indexing: A Semantic Segmentation Framework for Disease Severity Quantification in Field Crops [3.466303200807006]
  Plant diseases, resulting from both biotic and abiotic stresses, cause an estimated 20-40% loss in global agricultural yield annually.<n>This paper proposes a unified deep learning pipeline integrating semantic segmentation, regression-based severity estimation, and disease classification.
  arXiv  Detail & Related papers  (2026-07-05T06:04:20Z)
• A Multi-Domain Red Teaming Framework for Safety, Robustness, and Fairness Evaluation of Medical Large Language Models [34.38896321208398]
  Large language models (LLMs) are increasingly deployed across healthcare, yet existing benchmarks fail to capture model behavior under adversarial or ethically complex conditions.<n>We developed a multi-domain red teaming framework evaluating eleven contemporary LLMs across 690 clinically grounded scenarios.<n>Results revealed substantial performance variance, with mean scores ranging from 0.791 to 0.984.
  arXiv  Detail & Related papers  (2026-04-15T14:57:55Z)
• Severe Domain Shift in Skeleton-Based Action Recognition:A Study of Uncertainty Failure in Real-World Gym Environments [0.0]
  We present a systematic study of a severe domain shift using a novel Gym2D dataset and the UCF101 dataset.<n>Our work challenges standard deployment assumptions, providing a principled safety analysis of both semantic and geometric skeleton recognition deployment.
  arXiv  Detail & Related papers  (2026-03-16T17:37:17Z)
• Margin-Consistent Deep Subtyping of Invasive Lung Adenocarcinoma via Perturbation Fidelity in Whole-Slide Image Analysis [15.00702842825464]
  Whole-slide image classification for invasive lung adenocarcinoma subtyping remains vulnerable to real-world imaging perturbations.<n>We propose a margin consistency framework evaluated on 203,226 patches from 143 whole-slide images.<n>Our approach achieves robust feature-logit space alignment measured by Kendall correlations of 0.88 during training and 0.64 during validation.
  arXiv  Detail & Related papers  (2026-02-27T20:47:15Z)
• When LLMs get significantly worse: A statistical approach to detect model degradations [33.63321816712603]
  Minimizing the inference cost and latency of foundation models has become a crucial area of research.<n>We propose a statistically sound hypothesis testing framework based on McNemar's test allowing to efficiently detect model degradations.<n>We find that with our tests even empirical accuracy degradations of 0.3% can be confidently attributed to actual degradations rather than noise.
  arXiv  Detail & Related papers  (2026-02-09T10:45:13Z)
• Selective Diabetic Retinopathy Screening with Accuracy-Weighted Deep Ensembles and Entropy-Guided Abstention [0.0]
  Diabetic retinopathy (DR) is projected to affect more than 130 million individuals worldwide by 2030.<n>Deep ensemble learning framework integrated with uncertainty estimation is introduced to improve robustness, transparency, and scalability in DR detection.<n>Training and validation on 35,000 EyePACS retinal fundus images produced an unfiltered accuracy of 93.70 percent.
  arXiv  Detail & Related papers  (2025-10-29T04:16:04Z)
• Structural damage detection via hierarchical damage information with volumetric assessment [1.4470320778878742]
  Structural health monitoring (SHM) is essential for ensuring the safety and longevity of infrastructure.<n>This study introduces the Guided Detection Network (Guided-DetNet), a framework designed to address these challenges.<n>Guided-DetNet is characterized by a Generative Attention Module (GAM), Hierarchical Elimination Algorithm (HEA), and Volumetric Contour Visual Assessment (VCVA)
  arXiv  Detail & Related papers  (2024-07-29T04:33:04Z)
• Enhancing Diagnostic Reliability of Foundation Model with Uncertainty Estimation in OCT Images [41.002573031087856]
  We developed a foundation model with uncertainty estimation (FMUE) to detect 11 retinal conditions on optical coherence tomography ( OCT) FMUE achieved a higher F1 score of 96.76% than two state-of-the-art algorithms, RETFound and UIOS, and got further improvement with thresholding strategy to 98.44%. Our model is superior to two ophthalmologists with a higher F1 score (95.17% vs. 61.93% &71.72%)
  arXiv  Detail & Related papers  (2024-06-18T03:04:52Z)
• MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers [41.56951365163419]
  "MixedNUTS" is a training-free method where the output logits of a robust classifier are processed by nonlinear transformations with only three parameters. MixedNUTS then converts the transformed logits into probabilities and mixes them as the overall output. On CIFAR-10, CIFAR-100, and ImageNet datasets, experimental results with custom strong adaptive attacks demonstrate MixedNUTS's vastly improved accuracy and near-SOTA robustness.
  arXiv  Detail & Related papers  (2024-02-03T21:12:36Z)
• Conservative Prediction via Data-Driven Confidence Minimization [70.93946578046003]
  In safety-critical applications of machine learning, it is often desirable for a model to be conservative. We propose the Data-Driven Confidence Minimization framework, which minimizes confidence on an uncertainty dataset.
  arXiv  Detail & Related papers  (2023-06-08T07:05:36Z)
• Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification [71.06194656633447]
  We establish an uncertainty-inspired open-set (UIOS) model, which was trained with fundus images of 9 retinal conditions. Our UIOS model with thresholding strategy achieved an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set. UIOS correctly predicted high uncertainty scores, which would prompt the need for a manual check in the datasets of non-target categories retinal diseases, low-quality fundus images, and non-fundus images.
  arXiv  Detail & Related papers  (2023-04-08T10:47:41Z)
• The Report on China-Spain Joint Clinical Testing for Rapid COVID-19 Risk Screening by Eye-region Manifestations [59.48245489413308]
  We developed and tested a COVID-19 rapid prescreening model using the eye-region images captured in China and Spain with cellphone cameras. The performance was measured using area under receiver-operating-characteristic curve (AUC), sensitivity, specificity, accuracy, and F1.
  arXiv  Detail & Related papers  (2021-09-18T02:28:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.