## 5 Results and Discussion
### Comparison of Training Processes and Evaluation Indexes  
To evaluate the potential impact of replacing the \(3\times 3\) convolution kernel with DSC on the performance of network models, we trained ResNet18, ResNet50 [48], VGG16 [49], Inception-v3 [50], DenseNet121 [31], and DenseNet121 using DSC under the same dataset and experimental setup. The values of accuracy and loss on the testing dataset were recorded after each iteration of each model during the training process, as shown in Figure 14.  
The results demonstrate that the accuracy of the five models increases as the number of iterations grows. Most models achieve stability after several iterations, typically about 20 epochs, except VGG16, which gradually reaches stability after approximately 80 iterations. DenseNet121 exhibited the fastest rise and attained stability after several iterations. Interestingly, the rising trend and convergence of accuracy of DenseNet121 using DSC were essentially the same as that of DenseNet121 itself. These findings indicate that replacing the convolution kernel with DSC did not negatively impact the performance of the model. Although ResNet50 contains more parameters than ResNet18, the accuracy of ResNet18 after convergence remains stable at 100%, whereas that of ResNet50 stabilizes at 98.50%. This suggests that a higher number of parameters do not guarantee better model performance. Moreover, a larger model may contain redundant parameters. This is one of the reasons we opted to modify the model. The accuracy of VGG16 and Inception-v3 also stabilizes at 98.5%, demonstrating that an increase in parameter quantity is not necessarily the only way to enhance model performance. Table 3 illustrates that after implementing DSC, the parameter quantity, model size, and FLOPs of DenseNet121 reduced to 73.78%, 74.54%, and 63.30%, respectively. This reduction did not affect the model's performance, indicating that the use of DSC can effectively reduce the number of parameters and computational complexity without sacrificing the accuracy of the original model. Although DenseNet is a dense connection model that achieves ResNet's performance with fewer parameters through feature reuse, its million  
Figure 14: Curves of accuracy and loss during training of different models. parameters quantity and \(1\times 10^{9}\)-FLOPs remain high. Therefore, a modified DenseNet was developed for identifying blind zone defects. After conducting numerous experiments, it was determined that constructing Dense-DSC-0 with four dense blocks, each containing 3, 6, 8, and 4 dense layers, provides optimal results. To minimize the impact of model modification, an attention module was added before the classification network of Dense-DSC-0. The effects of five attention mechanisms: efficient channel attention (ECA) [51], squeeze and extraction (SE) [52], CAM, SAM, and CBAM, and additionally, three lightweight models: SqueezeNet [53], ShuffleNet-v2 [54], and MobileNet-v3-small [55], were compared. Figure 15 shows the accuracy and loss curve of the testing dataset after each iteration of the model with different attention mechanisms during the training process. As the performances of the lightweight models were significantly weaker than that of the model with an attention mechanism, they are not shown in Figure 15.  
According to Figure 15, it is evident that the modified DenseNet model, along with the added attention module, faces initial difficulty as it struggles to determine the appropriate direction. However, the accuracy rate gradually improves after ten epochs, indicating a better understanding of crucial feature information. The model's accuracy eventually stabilizes after approximately 30 to 40 iterations. It is worth noting that these models' convergence speed is relatively weaker than common models due to the simplified model, which impacts the feature information's learning speed. Nevertheless, the loss curve demonstrates that the model's maximum amplitude of oscillation decreases after the model is modified.