面向多类工业异常检测的中心感知残差异常合成
Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection
AI总结：
提出中心感知残差异常合成方法，通过统一中心与距离引导合成，解决多类工业异常检测中的类间干扰和类内重叠问题，提升检测精度。
摘要：
异常检测在工业图像检测中起着至关重要的作用。现有的大多数方法需要为每个类别单独训练模型，导致部署成本成倍增加。这凸显了开发用于多类异常检测的统一模型的挑战。然而，类间干扰的显著增加会导致严重的漏检。此外，正常样本与异常样本之间的类内重叠，尤其是在基于合成的方法中，不容忽视，并可能导致过检。为解决这些问题，我们提出了一种新颖的用于多类异常检测的中心感知残差异常合成（CRAS）方法。CRAS利用中心感知残差学习将不同类别的样本耦合到一个统一的中心，以减轻类间干扰的影响。为进一步减少类内重叠，CRAS引入了距离引导的异常合成，该合成根据正常数据分布自适应地调整噪声方差。在多种数据集和真实工业应用上的实验结果表明，CRAS具有优越的检测精度和有竞争力的推理速度。源代码和新构建的数据集可在https://github.com/cqylunlun/CRAS公开获取。
英文摘要：
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.
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。
