Image-based systems have gained popularity owing to their capacity to provide rich manufacturing status information, low implementation costs and high acquisition rates. However, the complexity of the image background and various anomaly patterns pose new challenges to existing matrix decomposition methods, which are inadequate for modeling requirements. Moreover, the uncertainty of the anomaly can cause anomaly contamination problems, making the designed model and method highly susceptible to external disturbances. To address these challenges, we propose a two-stage strategy anomaly detection method that detects anomalies by identifying suspected patches (Ano-SuPs). Specifically, we propose to detect the patches with anomalies by reconstructing the input image twice: the first step is to obtain a set of normal patches by removing those suspected patches, and the second step is to use those normal patches to refine the identification of the patches with anomalies. To demonstrate its effectiveness, we evaluate the proposed method systematically through simulation experiments and case studies. We further identified the key parameters and designed steps that impact the model's performance and efficiency.

翻译：基于图像的系统因其能够提供丰富的制造状态信息、低实施成本和高采集速率而日益受到青睐。然而，图像背景的复杂性及多样的异常模式给现有矩阵分解方法带来了新的挑战，这些方法难以满足建模需求。此外，异常的不确定性会引发异常污染问题，导致所设计的模型和方法极易受到外部干扰。针对上述挑战，我们提出了一种两阶段异常检测方法——通过识别疑似斑块（Ano-SuPs）检测异常。具体而言，我们通过对输入图像进行两次重构来检测异常斑块：第一步通过移除疑似斑块获取一组正常斑块，第二步利用这些正常斑块精化对异常斑块的识别。为验证方法有效性，我们通过仿真实验和案例研究系统性地评估了所提方法，并进一步识别了影响模型性能与效率的关键参数及设计步骤。