## 4 Experimental Results and Comparisons
### Experiments on the Benchmark Dataset NEU
#### 4.1.3 The Effects of Increasing the Percentage of Noisy Labels in the Training Set  
In this experiment, noisy labels were artificially added to the training set of the NEU dataset after data augmentation by adopting the following steps:  
Step 1: According to the given noise ratio (percentage of noisy labels), namely Noise-Level, select K samples from N total samples:  
\[\text{K}=\text{N}\times\text{NoiseLevel} \tag{9}\]  
Step 2: For each category of samples in the selected K samples, replace its original label with a random number between 0 and 6 (six typical steel surface defects) after deducting the original label (i.e., the truth label).  
Figure 7 shows the trends in defects classification accuracy of standard Inception_v2 and the proposed method when the proportion of noisy labels in the training set increases from 0% to 100%. It can be observed from the figure that even the standard Inception_v2 can maintain the classification accuracy of the test set above 80% when the percentage of noisy labels in the training set does not exceed 60%. The most plausible explanation for this is that the labels are basically uniformly distributed in the NEU dataset. For each label, the original label was replaced with one of the other five random numbers, the noisy labels were evenly distributed among the other five categories, and the number of truth labels was significantly higher than the number of noisy labels. When the percentage of noisy labels increased further, although the percentage of truth labels was still higher than that of the noisy labels, the quantitative advantages started to be reduced. We calculated the current truth labels in one category and the noisy labels distributed in other categories by increasing the percentage of noisy labels; the results are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Defects** & **Accuracy (\%)** & **Sensitivity (\%)** & **Specificity (\%)** \\ \hline Cr & 90.59 & 90.10 & 90.69 \\ In & 89.94 & 88.92 & 90.14 \\ Pa & 90.36 & 89.72 & 90.49 \\ PS & 89.53 & 88.29 & 89.76 \\ RS & 90.15 & 89.33 & 90.31 \\ Sc & 90.28 & 89.57 & 90.41 \\ Average & 90.14 & 89.32 & 90.31 \\ \hline \end{tabular}
\end{table}
Table 4: The accuracy, sensitivity, and specificity of each defect of proposed method with the NEU dataset after data augmentation.  
It can be observed from the table that the classification accuracy of proposed method is considerably higher than that of the standard Inception_v2 when the percentage of noisy labels does not exceed 60%. When the percentage of noisy labels exceeds 60%, the classification accuracy of both standard inception_v2 and the proposed method drop rapidly. The results demonstrate the strong robustness of the proposed method in the presence of noisy labels in model training. In actual production, more than 85% of the defect classification accuracy is required. The standard inception_v2 barely meets the requirement when the percentage of noisy labels in the training set reaches 20% and fails to meet the requirement when it exceeds 20%; however, the proposed method can meet the requirement well when the percentage of noisy labels reaches 40% and even maintains a classification accuracy higher than 85% when the percentage of noisy labels exceeds 50%.