## 7 Conclusions  
The paper presents the processes of rapid screening surface, defect feature extraction, class balance, sample data augmentation, defect detection and classification. This is a set of processes required to complete strip steel defect detection. With these, we can meet the basic requirements of online real-time detection. However, there are still some limitations in this paper. With continued production line developments, various kinds of new defect types will be created. When there is a similarity between a new defect form and a known defect form, the model will make a misjudment, which will greatly reduce the accuracy of defect recognition. To address this problem, solution in this paper is continuing to collect new defect images to update the database and retrain the model offline. Regularly updating the model deployment to the production line will ensure the model is accurate on new types of defects. In summary, this paper proposed _An Improved VGG19 Transfer Learning Strip Steel Surface Defect Recognition Deep Neural Network Based on Few Samples and Imbalanced Datasets_, which has a strong generalization and good convergence. It is a non-contact and non-destructive machine vision detection method that has the advantages of a high detection rate, strong real-time performance, and perfect anti-interference. For the differences between the baseline VGG19 and the improved VGG19, we compared their confusion matrix, precision, recall, and F1-score of the two models in Figure 17 and Table 2. The improved VGG19 network performs slightly better than the baseline VGG19 in six types of defects, but the improved VGG19 performs significantly better in the surface seams defects. The experimental results show that the improved VGG19 network proposed in this paper has high practicability and reliability; it has a certain practical value for strip steel defects detection. In view of the limitations of research content, it is hoped that machine vision can be combined with various sensor technologies to achieve the comprehensive evaluation of multi-modal data fusion for defect detection \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Accuracy** & **Time per Image** \\ \hline M-Pooling CNN [17] & 93.37\% & 0.007s \\ [PERSON] et al., 2012 & & 142.9 FPS \\ \hline CNN [48] & 98.10\% & 0.0021 s \\ [PERSON] et al., 2019 & & 476.2 FPS \\ \hline PLCNN [18] & 94.74\% & 0.00865 s \\ [PERSON] et al., 2020 & & 115.6 FPS \\ \hline ResNet50+MFN [22] & & \\ ResNet34+MFN & 99.70\% & 0.165 s/6.1 FPS \\ [PERSON] et al., 2020 & & 0.115 s/8.7 FPS \\ \hline Ours & 97.62\% & 0.0192 s/52.1 FPS \\ \hline \hline \end{tabular}
\end{table} 
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* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Improved sobel algorithm for defect detection of rail surfaces with enhanced efficiency and accuracy. _J. Cent. South. Univ._**2016**, _23_, 2867-2875. [CrossRef]
* [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A surface defects inspection method based on multidirectional gray-level fluctuation. _Int. J. Adv. Robot. Syst._**2017**, _14_, 1-17. [CrossRef]
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