## 5 Strip Steel Surface Defect Recognition Deep Neural Network Based on Transfer Learning
### A Transfer Learning Deep Neural Network Based on Improved VGG19
RAdam has the advantages of both Adam and SGD, which can not only ensure fast convergence speed but also avoid dropping into the local optimal, as the convergence result is insensitive to the network's initial value learning rate. RAdam has a better performance than SGD in the case of a network having a large learning rate and limited training dataset. Figure 15 shows that the convergence and accuracy of the network was greatly improved on the basic network. The model occasionally had a sudden increase in loss during the training period, but the verification function's loss decreased and tended to converge as the training progressed. After the test, the final accuracy of the model converged to 97.8% and the generalization and robustness had been greatly improved, which had a certain practical value. Figure 16 shows the comparison of the performance of the two algorithms, baseline VGG19 and improved VGG19, more intuitively, with the accuracy and loss curves of these two algorithms put into a unified ordinate system. Figure 16: The accuracy and loss error differences between the baseline VGG19 and improved VGG19 network. Figure 15: The accuracy and loss error of improved VGG19 network. Figure 17 shows the baseline VGG19 and improved VGG19 network models' confusion matrix on the test dataset. In order to be closer to actual production, the ROI region image augmentation algorithm in this paper was not augmented to achieve sample balance in categories on the testing dataset. It can be seen that both the baseline VGG19 and the improved VGG19 model can fully identify categories of cracks, scabs, and rolled in scales. These three types of defects have obvious features and are easy for the model to recognize. For the defect categories of inclusions, the baseline VGG19 misidentified four pieces of pictures and the improved VGG19 model misidentified three pieces of pictures. For the defect of pitted surfaces, both the baseline VGG19 and the improved VGG19 model misidentified one piece of picture. For the defect categories of surface scratch, the baseline VGG19 misidentified four pieces of pictures and the improved VGG19 model misidentified three pieces of pictures. The improved VGG19 network performs slightly better than the baseline VGG19 in these six types of defects, but the improved VGG19 performs significantly better in the surface seams defects. However, the surface seams dataset has only 40 pieces of pictures and only 10 pieces of pictures for the testing dataset. Therefore, the accuracy of the baseline VGG19 and the improved VGG19 model on surface seams dataset are 70% and 90%, respectively. These indicators can be seen in the classification test report in Table 2. \begin{table}
\begin{tabular}{c c|c|c|c} \hline \hline
**Defects** & **Targets** & **Precision** & **Recall** & **f1-Score** & **Samples** \\ \hline \multicolumn{5}{c}{Baseline VGG19 Network} \\ \hline Cracks & 1.0000 & 1.0000 & 1.000 & 60 \\ \hline Inclusion & 0.9032 & 0.9333 & 0.9180 & 60 \\ \hline Scab & 1.0000 & 1.0000 & 1.0000 & 60 \\ \hline Pitted Surface & 1.0000 & 0.9833 & 0.9916 & 60 \\ \hline Rolled in Scale & 1.0000 & 1.0000 & 1.0000 & 60 \\ \hline Surface Scratch & 0.9333 & 0.9333 & 0.9333 & 60 \\ \hline Surface Seams & 0.7778 & 0.7000 & 0.7368 & 10 \\ \hline Weighted Avg & 0.9675 & 0.9676 & 0.9674 & 370 \\ \hline \end{tabular}
\end{table} 
Table 2: Report on the test dataset. Figure 17: Confusion matrix of the test dataset.