arXiv 2512.114012025-12-12cs.CV

多类工业异常检测的协同重建与修复

Collaborative Reconstruction and Repair for Multi-class Industrial Anomaly Detection

College of Intelligent Robotics and Advanced Manufacturing, Fudan University（智能机器人与先进制造学院，复旦大学）；College of Physics and Electromechanical Engineering, Hexi University（物理与机电工程学院，河西大学）；College of Design and Innovation, Tongji University（设计与创新学院，同济大学）；Shanghai Key Lab of Intelligent Information Processing, College of Computer Science and Artificial Intelligence, Fudan university（智能信息处理上海重点实验室，计算机科学与人工智能学院，复旦大学）；School of Biomedical Engineering, Tsinghua University（生物医学工程学院，清华大学）

Qishan Wang, Haofeng Wang, Shuyong Gao, Jia Guo, Li Xiong, Jiaqi Li, Dengxuan Bai, Wenqiang Zhang

备注：: Accepted to Data Intelligence 2025

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AI总结：

本研究提出协同重建与修复框架，通过优化解码器和引入特征遮蔽技术，有效解决工业异常检测中的身份映射问题，提升多类异常检测性能。

摘要：

工业异常检测是一个具有挑战性的开放集任务，旨在识别偏离正常数据分布的未知异常模式。为了避免因为每个类别构建单独模型而带来的显著内存消耗和有限的泛化能力，我们专注于开发一个多类异常检测的统一框架。然而，在这种具有挑战性的设置下，传统基于重建的网络往往面临身份映射问题，即它们直接复制输入特征，无论这些特征是正常还是异常，导致检测失败。为了解决这个问题，本研究提出了一种新的框架，称为协同重建与修复（CRR），将重建转换为修复。首先，我们优化解码器以重建正常样本并修复合成的异常。因此，它为异常区域生成不同的表示，而为正常区域生成与编码器输出相似的表示。其次，我们实现了特征级随机遮蔽，以确保解码器的表示包含足够的局部信息。最后，为了最小化编码器和解码器特征表示之间的差异所带来的检测误差，我们训练了一个由合成异常掩码监督的分割网络，从而提高定位性能。在工业数据集上的大量实验表明，CRR有效缓解了身份映射问题，并在多类工业异常检测中实现了最先进的性能。

英文摘要：

Industrial anomaly detection is a challenging open-set task that aims to identify unknown anomalous patterns deviating from normal data distribution. To avoid the significant memory consumption and limited generalizability brought by building separate models per class, we focus on developing a unified framework for multi-class anomaly detection. However, under this challenging setting, conventional reconstruction-based networks often suffer from an identity mapping problem, where they directly replicate input features regardless of whether they are normal or anomalous, resulting in detection failures. To address this issue, this study proposes a novel framework termed Collaborative Reconstruction and Repair (CRR), which transforms the reconstruction to repairation. First, we optimize the decoder to reconstruct normal samples while repairing synthesized anomalies. Consequently, it generates distinct representations for anomalous regions and similar representations for normal areas compared to the encoder's output. Second, we implement feature-level random masking to ensure that the representations from decoder contain sufficient local information. Finally, to minimize detection errors arising from the discrepancies between feature representations from the encoder and decoder, we train a segmentation network supervised by synthetic anomaly masks, thereby enhancing localization performance. Extensive experiments on industrial datasets that CRR effectively mitigates the identity mapping issue and achieves state-of-the-art performance in multi-class industrial anomaly detection.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。