## 4 Results
### Qualitative Evaluation  
Images generated by the GAN, DCGAN, and our SAITI-DCGAN approach are illustrated in Figure 7. In the left-most column, various types of infrared thermal images of aluminum foil sealing are displayed. Specifically, it includes images of broken aluminum foil within the bottle cap (BAF), images that depict the absence of aluminum foil in the bottle cap (MAF), and images showcasing instances where the bottle cap was either inadequately tightened or skewed during the tightening process (LCC). The final row presents the generation outcomes obtained from the GAN, DCGAN, and our proposed SAITI-DCGAN approach. Given the large number of test samples, for each type of defect generation, the input used for comparison in Figure 7 was randomly selected from the normal samples in the test set. All samples were rigorously screened and classified under the guidance of domain experts, ensuring that they not only accurately reflected both normal and abnormal conditions in real-world application scenarios but also maintained internal consistency within each category of images. Consequently, even randomly selected samples, particularly the normal samples, possessed characteristics that were representative of their respective categories. Furthermore, to ensure the fairness and directness of the model output comparisons, all tests were conducted at the same iteration stage on identical inputs. This meant that the different network architectures faced exactly the same input conditions at the same training stage, thereby making the comparison of output results more equitable and comparable. The results shown are the outputs of the networks at different iteration stages for the same input. Further analysis of these results will be provided based on the subsequent experimental findings. We can observe that in the GAN model, during the initial 100 training iterations for the three defect cases (i.e., BAF, MAF, and LCC), the generated images exhibited significant blurriness and noise, lacking distinct forms for the defects. After 1000 iterations, the generated images still retained some blurriness, but they showcased enhanced content and sharpness compared to the 100 iteration stage. By the time the training reached 1500 iterations, the generated images began to exhibit the rudimentary form of the defects. With further training iterations, at around 2000 iterations, the generated images became clearer, containing richer detail information than that at 1500 iterations. The images produced after 2000 iterations were close to the actual defect images. With the original DCGAN model, the internal defect structure of the generated images from 100 to 500 training iterations remained chaotic. Stability in shape was gradually achieved \begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Description** & **Example** \\ \hline BAF & broken aluminum foil & \\ MAF & missing aluminum foil & \\ LCC & loose or crooked caps & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Collected dataset images of aluminum foil sealing. around 750 training iterations, occasionally accompanied by aberrant images. This pattern was particularly evident in the BAF and LCC cases, while well-defined MAF defect images emerged as the training progressed to 1750 iterations. In comparison, the generated images of our SATII-DCGAN yielded a stable and clear structure after approximately 750 training iterations with respect to all three categories. In evaluating the generated anomaly samples, we collaborated with industry experts to conduct a detailed comparative analysis. Leveraging their extensive experience in defect detection and image processing, the experts provided critical guidance and feedback throughout the process. Their input was essential in ensuring the scientific rigor and reliability of our findings. Firstly, the GAN model was easily influenced by noise during the training process. Moreover, the images generated by the GAN for the three types of defects exhibited a monotonous style, lacking diversity and randomness.