# Quality Grading of Dried Abalone Using an Optimized VGGNet
## 4 Results and Discussion  
### Comparison of Network Accuracy  
To investigate the effect of classification networks on the internal quality grading of dried abalone, we conducted tests on the dried abalone database using five original networks: VGG-16, VGG-19, MobileNet, AlexNet, and Xception. Table 2 presents the comparison result, VGG-19 network achieved an accuracy of 94.21% for the internal quality grading of dried abalone samples, which was significantly higher than that of the other networks. This outcome can be attributed to several factors: MobileNet is tailored for mobile and embedded devices, prioritizing speed over accuracy, which leads to a reduced number of layers and consequently lower recognition precision; Xception, while having more layers than MobileNet, incorporates depthwise separable convolutions that substantially decrease computational requirements, allowing it to maintain a reasonable level of accuracy. The VGG-19 model boasts the deepest architecture, which is conducive to high-precision image classification tasks. VGG-16 and AlexNet, with slightly fewer layers than VGG-19, exhibited slightly lower recognition accuracy in comparison to VGG-19. These results underscore the importance of network architecture in achieving accurate quality grading for dried abalone. To further explore the impact of attention mechanisms and batch normalization (BN) on the VGG-19 network, we conducted a series of experiments to evaluate their effects on model performance. Attention mechanisms are expected to enhance feature extraction by focusing on relevant parts of the input data, potentially improving the accuracy and robustness of the model. Batch normalization, on the other hand, is known to stabilize the learning process and reduce the internal covariate shift, which can accelerate training and lead to better convergence. Table 3 shows the accuracy of VGG-19 using different components. Integrating BN into the VGG-19 network effectively enhanced the accuracy of the internal quality classification of dried abalones. The accuracy of VGG-19+SE did not improve, demonstrating that separate channel attention did not improve the classification accuracy. However, the accuracy of the VGG-19+CBAM model increased to 95.14%. CBAM can improve the accuracy of VGG-19 in the classification of dried abalone X-ray images. Additionally, since SE and CBAM are lightweight and general-purpose modules with a minimal increase in parameter count and computational load, their impact on training and inference times is negligible, and thus, this paper does not include a comparative analysis of parameter counts. Consequently, by comparing the convergence speed of the loss value and accuracy among the three networks during training, VGG-19+CBAM demonstrated a faster convergence speed than VGG-19 and VGG-19+SE. Figure 9 depicts the confusion matrices of the testing set for VGG-19, VGG-19+CBAM, and VGG-19+SE. VGG-19 exhibited errors in two categories: low accuracy for \"poor\" samples, likely due to a small sample size causing an imbalance, and misrecognition errors between \"good\" and \"average\" samples, reflecting their fine-grained similarities. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Error Rate (\%)** \\ \hline VGG-16 & 92.29 & 7.71 \\
**VGG-19** & **94.21** & **5.79** \\ MobileNet & 42.08 & 67.92 \\ AlexNet & 92.50 & 7.50 \\ Xception & 75.31 & 24.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparisons of the five networks. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Original** & **Using BN** \\ \hline VGG-19 & 94.21 & 94.30 \\ VGG-19+SE & 93.48 & 93.88 \\
**VGG-19+CBAM** & **94.37** & **95.14** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation study of VGG-19 with different components. Compared with VGG-19, both attention mechanisms enhanced the accuracy for \"good\" samples, which means that VGG-19 suffered the highest misjudgment in that category. In general, VGG-19+CBAM outperformed VGG-19, and VGG-19+SE had the lowest accuracy.