FuDU: A Fuzzy Dual-dimension Uncertainty Framework for Streaming Active Learning in Industrial Defect Detection

摘要

Ensuring the reliability of deep learning models in real-timeindustrial 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 paperproposes a streaming active learning method based on the Fuzzy Dual-dimensional Uncertainty (FuDU) framework. Specifically, we first designa Prototype-based Global Uncertainty Quantification (PGUQ) moduleon the backbone to evaluate image-level uncertainty via normal/defectivefeature prototypes. A Dual-entropy defect Uncertainty Evaluator (DeUE)is then integrated into the detection head to quantify box-level uncer-tainty. Finally, by modeling uncertainty as systematic error, we pro-pose a fuzzy dual-dimensional uncertainty-aware strategy that lever-ages fuzzy inference to fuse dual-dimensional uncertainties, enabling ex-pert knowledge-driven adaptive sampling decisions. Comprehensive ex-periments demonstrate that FuDU is efficient and flexible, making itwell-suited for challenging industrial inspection tasks such as the de-tection of nuclear fuel rod defects. Our code is publicly available at:https://github.com/wangzhaoyang-508/FuDU.