## 3 Results and Discussion  
In this study, we trained six deep learning models to determine the quality of cylindrical shells. The model conducted the assessment process in real time, so a fast and accurate model was needed. Figures 9 and 10 provide information on the accuracy gained from the entire model during the non-real-time testing process. Based on Figures 9 and 10, it can be seen that deep learning models obtained excellent accuracy when tested with testing data. VGG and ResNet, the predecessors of the deep learning model, obtained very satisfactory results. In contrast, MobileNet v2, the lightest model, produced the lowest accuracy for both the longitudinal and the bottom sides. This low accuracy was due to the MobileNetv2 architecture having the lowest number of parameters in performing high-level feature extraction properly [37]. Other issues must be considered when using deep learning in the real-world setting, namely the computational time and resources requirement. The computational time of a deep learning model can be seen through the number of parameters each model has [38]. Table 1 provides information about the parameters of each model. Based on Table 1, the model with the least number of parameters is MobileNet v2 and the one with the most is VGG. The model selection used in the application is based on the resulting accuracy and the number of parameters. Of the overall models we use, the most recent model is Efficient \begin{table}
\begin{tabular}{c c} \hline \hline
**Deep Learning Model** & **Parameter** \\ \hline VGG [22] & 138 M \\ ResNet [25] & 25 M \\ MobileNet [26] & 4.2 M \\ MobileNet v2 [27] & 3.4 M \\ EfficientNet-B0 [28] & 5.2 M \\ EfficientNetV2S [29] & 22 M \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of each deep learning model. Figure 10: Longitudinal Side Model Performance Results. Figure 9: Bottom Model Performance Results. NetV2. The model provides excellent accuracy, but in terms of the number of parameters, the model is not suitable for use because it has the third largest parameter. MobileNet v2 is claimed to be the lightest model, but this affects the accuracy produced. The resulting accuracy of MobileNet v2 is the lowest. In this study, we selected the MobileNet v1 model because it did not differ significantly from MobileNet v2, and the number of parameters was between Mobilenet v2 and EfficientNet. In addition, MobileNet v1 also had equally good accuracy when assessing the quality of cylindrical shells. However, when conducting quality assessments on the longitudinal side, the accuracy was lower than that of VGG, ResNet, and EfficientNetV2S. Although VGG, ResNet, and EfficientNetV2S have stable accuracy, the number of parameters is a constraint when implementing in the real world. The web and desktop-based application user interface we built can be seen in Figure 11. In Figure 11, there are eight frames. The eight frames display the captured results from the high frame rate camera sensor. Each camera has two frames for the top and bottom sides, while the longitudinal side consists of four frames. The longitudinal side is divided into four frames because the captured object is a cylindrical shell object. Each left and right side frame represents the part of the frame captured by the high frame rate camera sensor. Moreover, we split into two frames (for the top and bottom sides) and four frames specifically for the longitudinal side to optimize the image size processed by the deep learning model. This procedure was performed because the deep learning model used has been designed to accept inputs of the same size. In this study, we focused on the web-based application because it is more straightforward to use. A web-based application is simpler because it can be created centrally. This feature is preferred because if there is a resource replacement, only the existing link needs to be accessed. The use of a web-based application can also be performed locally. In this study, we used a web-based application and placed a web server locally.