Problem
Research questions and friction points this paper is trying to address.
real-time industrial defect detection
deep learning models
uncertain samples
industrial media streams
reliability
FuDU: A Fuzzy Dual-dimensional Uncertainty Framework for Streaming Active Learning in Industrial Defect Detection
🤖 AI Summary
为提高工业缺陷检测中深度学习模型的可靠性，本文提出基于模糊双维不确定性框架FuDU的流式主动学习方法，通过量化图像级和框级不确定性并融合专家知识进行自适应采样。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Fuzzy Dual-dimensional Uncertainty
Streaming Active Learning
Prototype-based Global Uncertainty Quantification
Dual-entropy defect Uncertainty Evaluator
🔎 Similar Papers
No similar papers found.
