# Industrial Image Anomaly Detection via Self-Supervised Learning with Feature Enhancement Assistance
## 4 Results
### Ablation Experiment
#### 4.3.4 Different Anomaly Simulation Strategies  
Finally, we investigated the impact of different anomaly simulation strategies on anomaly detection performance. As shown in Figure 7, there are a total of four different synthetic anomalies as follows: the first two is from CutPaste, involving randomly cutting a piece from the image and pasting it into another location, with rectangular blocks and small patches ('scar'), respectively. The last two are the anomaly simulation strategies in this paper, with no foreground mask and with foreground mask, respectively. Table 8 eluci  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **w/o Atten** & **+SE** & **+CBAM** & **+CA** \\ \hline Texture & (97.8, 95.1) & (97.3, 94.6) & (96.5, 94.7) & (98.2, 95.5) \\ Object & (93.9, 96.0) & (93.2, 96.2) & (93.4, 96.3) & (93.9, 96.5) \\ \hline Average & (95.2, 95.7) & (94.6, 95.7) & (94.4, 95.7) & (95.7, 96.2) \\ \hline \hline \end{tabular}
\end{table}
Table 6: The impact of different attentional mechanisms on anomaly detection performance on MVTec AD.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & **256d** & **200d** & **160d** & **100d** \\ \hline +SE & (95.4, 96.2) & (94.4, 95.9) & (95.2, 96.0) & (94.6, 95.7) \\ +CBAM & (95.2, 96.4) & (94.9, 96.3) & (94.6, 96.3) & (94.4, 95.7) \\ +CA & (95.1, 96.4) & (95.5, 96.6) & (94.9, 96.2) & (95.7, 96.2) \\ \hline \hline \end{tabular}
\end{table}
Table 7: The impact of different feature dimensions on anomaly detection performance on MVTec AD.  
dates the anomaly detection performance of self-supervised training using these different anomaly simulation strategies. The fourth column ('rect&scar') in the table indicates that the rectangular blocks and small patches were trained as a separate class for triple classification. The fifth column ('2-way') indicates a binary classification task using the anomaly simulation in this paper, without distinguishing between texture and structure class anomalies, focusing solely on normal vs. anomalous instances. Experimental results show that our anomaly simulation strategy outperforms rectangular blocks and small patches, obtaining the highest image-level AUC score of 95.7%, while the pixel-level is slightly lower than the strategy without a foreground mask by 0.2%. In the texture class dataset, the highest image-level and pixel-level AUC scores (98.7%, 96.7%) were obtained without the foreground mask. This is because, in the texture class dataset, the samples occupy the entire image and no longer distinguish between the background and the foreground, so the whole image is the foreground. Thresholding binarization of the image in this case could damage the original information of the image and affect the performance of anomaly detection. As for the object class dataset, the best scores (93.9%, 96.5%) were achieved using the foreground mask, and this is because we restricted the region of simulated anomalies to the target object instead of the background, thereby preventing the introduction of unnecessary noise into the background during anomaly generation. The random cut-and-paste method has a high probability of generating anomalies into the background, especially in the category of screw, where the target only occupies a small portion of the entire image, which affects the performance of anomaly detection. Additionally, we can see that anomalies simulated using Perlin noise tend to appear closer to real-world anomalies.  
Figure 7: Pseudo-anomaly images generated using different anomaly simulation strategies. From left to right are (**a**) normal sample (**b**) cut-paste with rectangular blocks (**c**) cut-paste with small patches (scar) (**d**) our method without foreground mask (**e**) our method using foreground mask.