# Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing
## 3 Experimental Evaluation
### Discussion  
The method proposed in this paper preserves the existing original data; at the same time, it generates additional data that cannot be extracted through the CNN network, and performs training based on the data, showing higher performance compared to the existing method. In addition, maintaining the original data without data preprocessing through  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Original**} & \multicolumn{3}{c}{**Proposed**} \\ \multicolumn{1}{c}{**Mobilenet**} & **Resnet-50** & **Resnet-101** & **Mobilenet** & **Resnet-50** & **Resnet-101** \\ \hline Mean IoU & 0.6066 & 0.6362 & 0.6230 & 0.6289 & 0.6670 & 0.6791 \\ \hline Class IoU (Vent spew error) & 0.2192 & 0.2773 & 0.2671 & 0.2624 & 0.3383 & 0.3621 \\ \hline Precision & 0.3769 & 0.4235 & 0.5967 & 0.3664 & 0.4817 & 0.5010 \\ \hline Recall & 0.3437 & 0.4478 & 0.3260 & 0.4801 & 0.5319 & 0.5662 \\ \hline F1-Score & 0.3595 & 0.4342 & 0.4217 & 0.4157 & 0.5055 & 0.5316 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Original/proposed training results for various underlying networks.  
Figure 14: Trained network–based test results; the left side of each image is the ground truth, while the right side is the inference result.  
Figure 13: Comparison of the mean IoU (validation set) per epoch between the original and the proposed methods.  
filters, such as height threshold values, shows robustness to additional error training in the future.  
The proposed method has improved precision, recall, and F1-score values, compared to the previous one, but it is not excellent given absolute value. The reasons for this are the (1) lack of data and (2) inclusion of human error.  
It takes a long time to collect enough tire error data of the same kind for training. The methodology presented in this paper also has the purpose of compensating for the lack of data. However, as the number of data increases, the absolute precision and recall values will increase.  
In the data labeling performed in this paper, a person directly measures the length of the vent spew error and labels the depth image data. Additionally, since the area of error data is determined by the person, it is inconsistent. For example, it is ambiguous whether only the pixels of the rubber hair where the error occurred is taken as the error area or the area around it. A more accurate error measurement and labeling method will improve the results.  
Furthermore, in the training process, the training loss converges to 0 in both methods, but the validation loss diverges after a certain epoch. Figure 15 shows the training/validation loss in the training processes of the original and proposed methods.  
This type of graph occurs because the error criteria and labeling for the vent spew error are ambiguous. The vent spew error is a vent protruding more than 2 mm from the tire surface, and the vents protruding from the tire surface are clustered around this 2 mm. Because differences in increments of 0.1 mm are difficult to recognize by the ordinary person and by the naked eye, data labels are ambiguous, unless vents larger than 2 mm are clearly identified. In addition, data according to the degree of inclination of the vent protrusion is not considered, so if it exceeds 2 mm but is attached to the tire surface, it can be recognized as a normal vent.  
Therefore, in future research, for improving precision, recall, and training loss, we will study the classification method that considers the accurate measurement criteria of vent spew error and the protrusion angle of the vent.