统一模型检测多类工业异常，减少误检漏检。
Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection
- 用中心感知残差学习融合多类别样本，降低类间干扰。
- 基于正常数据分布自适应调整噪声方差，减少类内重叠。
- 在真实工业场景中表现优异，适合部署于多品类质检。
异常检测在工业图像质检中至关重要。现有方法通常需为每类异常单独建模，导致部署成本倍增。这凸显了构建统一多类异常检测模型的挑战：类间干扰加剧易引发漏检；合成类方法中正常与异常样本类内重叠显著，可能导致过检。为此，本文提出一种新的中心感知残差异常合成（CRAS）方法。CRAS通过中心感知残差学习将不同类别的样本耦合至统一中心，缓解类间干扰。为进一步降低类内重叠，引入距离引导的异常合成机制，根据正常数据分布自适应调整噪声方差。在多个数据集及真实工业应用中的实验表明，CRAS在检测精度上表现更优，推理速度具有竞争力。源代码与新构建的数据集已公开于 https://github.com/cqylunlun/CRAS。
原文摘要 · Abstract (English)
Anomaly detection plays a vital role in the inspection of industrial images. Most existing methods require separate models for each category, resulting in multiplied deployment costs. This highlights the challenge of developing a unified model for multi-class anomaly detection. However, the significant increase in inter-class interference leads to severe missed detections. Furthermore, the intra-class overlap between normal and abnormal samples, particularly in synthesis-based methods, cannot be ignored and may lead to over-detection. To tackle these issues, we propose a novel Center-aware Residual Anomaly Synthesis (CRAS) method for multi-class anomaly detection. CRAS leverages center-aware residual learning to couple samples from different categories into a unified center, mitigating the effects of inter-class interference. To further reduce intra-class overlap, CRAS introduces distance-guided anomaly synthesis that adaptively adjusts noise variance based on normal data distribution. Experimental results on diverse datasets and real-world industrial applications demonstrate the superior detection accuracy and competitive inference speed of CRAS. The source code and the newly constructed dataset are publicly available at https://github.com/cqylunlun/CRAS.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。