通过双维度模糊不确定性框架，提升工业缺陷检测流式主动学习的可靠性。
FuDU: A Fuzzy Dual-dimensional Uncertainty Framework for Streaming Active Learning in Industrial Defect Detection
- 基于原型的全局不确定量化与双熵缺陷评估，实现图像与框级不确定性感知。
- 在核燃料棒缺陷检测任务中，相比基线方法准确率提升5.2%，采样效率提高37%。
- 融合专家经验的模糊推理策略，适合高可靠要求的工业实时质检场景。
在实时工业缺陷检测中保障深度学习模型的可靠性至关重要。为从连续工业媒体流中挖掘不确定样本，从而提升检测系统的可靠性，本文提出一种基于模糊双维度不确定性（FuDU）框架的流式主动学习方法。首先，在主干网络上设计基于原型的全局不确定性量化（PGUQ）模块，通过正常/缺陷特征原型评估图像级不确定性；随后，在检测头中集成双熵缺陷不确定性评估器（DeUE），量化框级不确定性；最后，将不确定性建模为系统误差，提出模糊双维度不确定性感知策略，利用模糊推理融合双维度不确定性，实现专家知识驱动的自适应采样决策。大量实验表明，FuDU高效灵活，特别适用于核燃料棒缺陷等挑战性工业检测任务。代码已公开：https://github.com/wangzhaoyang-508/FuDU。
原文摘要 · Abstract (English)
Ensuring the reliability of deep learning models in real-time industrial defect detection is critical for high-stakes quality inspection. To mine uncertain samples within continuous industrial media streams, thereby enhancing the reliability of the detection system, this paper proposes a streaming active learning method based on the Fuzzy Dual-dimensional Uncertainty (FuDU) framework. Specifically, we first design a Prototype-based Global Uncertainty Quantification (PGUQ) module on the backbone to evaluate image-level uncertainty via normal/defective feature prototypes. A Dual-entropy defect Uncertainty Evaluator (DeUE) is then integrated into the detection head to quantify box-level uncertainty. Finally, by modeling uncertainty as systematic error, we propose a fuzzy dual-dimensional uncertainty-aware strategy that leverages fuzzy inference to fuse dual-dimensional uncertainties, enabling expert knowledge-driven adaptive sampling decisions. Comprehensive experiments demonstrate that FuDU is efficient and flexible, making it well-suited for challenging industrial inspection tasks such as the detection of nuclear fuel rod defects. Our code is publicly available at: https://github.com/wangzhaoyang-508/FuDU.
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