## 5 Conclusions  
In this article, we introduce a novel approach to tackle the challenges faced by OOD segmentation algorithms in industrial anomaly detection. Our method revolves around a  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Settings**} & \multicolumn{2}{c}{**Image-Level Metrics**} & \multicolumn{2}{c}{**Pixel-Level Metrics**} \\  & **AUROC** & **F1-Score** & **AUROC** & **F1-Score** \\ \hline Cflow & 65.78\% & 46.15\% & 88.64\% & 6.84\% \\ Filtered Cflow \#1 & 80.26\% & 75.00\% & 93.41\% & 13.32\% \\ Filtered Cflow \#2 & 81.05\% & 78.26\% & 93.58\% & 13.39\% \\ Filtered Cflow \#3 & 81.05\% & 78.26\% & 93.58\% & 13.39\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter analysis for settings of augmentation strategy.  
post-processing module, meticulously designed to discern intricate distinctions between false alarms and genuine defects within specific industrial applications.  
Furthermore, we present an innovative false alarm sampling strategy for OOD segmentation models. This strategy empowers us to generate essential training samples without additional labeled images.  
Our experimental results are comprehensive, entailing the evaluation of two advanced unsupervised anomaly detection algorithms, Cflow and FastFlow, across two distinct industrial applications. We also provide a parameter analysis experiment, shedding light on the augmentation settings employed on the two datasets. The results from these experiments unequivocally affirm the efficacy of the proposed method.  
The proposed method is founded on the presumption of analogous patch-level false alarm patterns between the training and testing datasets. Consequently, its applicability is constrained by the specificity of this characteristic.  
By addressing the challenges in industrial anomaly detection, our work contributes to the efficient false alarm elimination of unsupervised deep learning models. Our work specifically focuses on providing practical enhancements to anomaly detection tailored for industrial applications, leveraging the nuances of fuzzy domain knowledge. Future endeavors will extend to exploring false alarm elimination across a broader spectrum of state-of-the-art methods. We aim to delve into the underlying causes of high activations on anomaly maps, seeking a deeper understanding. Additionally, we will strive to enhance detection performance without relying on knowledge beyond the anomaly-free training dataset.  
J.Q.: conceptualization, methodology, software, validation, analysis, writing; H.S.: methodology, resources, analysis, writing, funding acquisition; Y.H.: conceptualization, methodology, analysis, validation; Z.Y.: methodology, supervision, funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Fundamental Research Funds for the Central Universities (Science and technology leading talent team project 2022 JBYT005), the State Key Laboratory of Advanced Rail Autonomous Operation (Contract No. RAO2023 ZZ003), and the Technology Development Program of China Energy Investment Corporation (Grant GJNY-21-65 and Grant GJNY-20-139).  
Not applicable.  
Data Availability Statement:Data are contained within the article.  
The authors declare no conflict of interest.