## 4 Experiments
### Visual Comparisons for Pixel-Wise Segmentation  
As depicted in Figures 6-9, the top two rows illustrate false alarms on anomaly-free images, and our proposed method effectively identifies and eliminates false alarm pixels. Despite the heterogeneity in the distribution of false alarms, our proposed method adaptively enhances the results. The bottom rows in these figures showcase defects in the test images after filtering, highlighting the discrimination capability of the post-processing model. Figures 6-9 provide intuitive examples of the impact of our method on two OOD models. These examples validate the points made in this paper, thoroughly confirming the effectiveness of the proposed approach. Since this paper primarily addresses the issue of false alarms in industrial anomaly detection, and the occurrence probability of anomalies themselves is exceedingly low, we have selected two examples from each experimental result: one showcasing the effects on anomaly-free images (negative) and one displaying the detection performance on a defect (positive) test image. Figure 6: Visualization of comparative experiments on wood defect examination using Fastflow. Columns correspond to: (**a**) the test image, (**b**) ground truth, (**c**) anomaly map from the baseline OOD model, (**d**) baseline defect mask, (**e**) baseline segmentation result, (**f**) filtered mask, and (**g**) filtered segmentation result. Figure 7: Visualization of comparison experiments on wood defect examination using Cflow. Columns correspond to: (**a**) the test image, (**b**) ground truth, (**c**) anomaly map from the baseline OOD model, (**d**) baseline defect mask, (**e**) baseline segmentation result, (**f**) filtered mask, and (**g**) filtered segmentation result. Specifically, in the first and second rows of Figure 6, the baseline FastFlow generates false alarms in the form of small blocks during prediction. These false alarms are successfully filtered out by our method. In the third row, which presents a combined defect image containing three abnormal regions, the baseline generates more predictions, many of which are disconnected small pixel blocks. It is evident that our method eliminates these small false positives while preserving true positive predictions, with no impact on the recall rate. Similarly, Figure 7 illustrates the performance of Cflow on these three images. The false alarms generated by Cflow exhibit some similarity to those of Fastflow. One possible explanation is that both models employ feature extraction networks trained on large-scale models, possibly leading to strong responses to pixel changes in the corresponding regions of the image. As this phenomenon exists in both the training and test sets, our method effectively captures and eliminates this type of false alarm. As indicated by the performance metrics in Table 3, false alarms are quite frequent in the TFDS-RP dataset, especially at the object level. In Figure 8, FastFlow generates numerous small predictions on defect-free images. These false alarms may be attributed to the complexity of the scene. In this scenario, pixel variations in some local regions  
Figure 8: Visualization of comparison experiments on the round pin examination of a freight train using Fastflow. Columns correspond to: (**a**) the test image, (**b**) ground truth, (**c**) anomaly map from the baseline OOD model, (**d**) baseline defect mask, (**e**) baseline segmentation result, (**f**) filtered mask, and (**g**) filtered segmentation result. Figure 9: Visualization of comparison experiments on a round pin examination of a freight train using Cflow. Columns correspond to: (**a**) the test image, (**b**) ground truth, (**c**) anomaly map from the baseline OOD model, (**d**) baseline defect mask, (**e**) baseline segmentation result, (**f**) filtered mask, and (**g**) filtered segmentation result. may be caused by factors such as lighting differences, component aging, and complex backgrounds. However, FastFlow captures these drastic changes in adjacent pixels and predicts them as positive.