## 4 Experiments and Discussion
### Experimental Results  
Visual Evaluation. The visual result of the model evaluation is shown in the Figure 5. We can accurately identify whether the surface of the object is defective and display its label on the image. Good means that the surface is free of defects; bad means that there are defects. Figure 4: Defects detect data set. In addition, in order to verify the effectiveness of the proposed MobileNet-v2-dense model, we compare the performance of the model to other latest three CNN-based defect detection algorithms, in terms of TNR/TPR and AUC-ROC indicators. AUC-ROC Evaluation. We compare the AUC-ROC of defect detection results to [PERSON]'s method [21] on the task of detecting defects in DAGM test dataset, which is part of the DAGM dataset. The results are shown in Table 3. From these results, we see that our model has higher AUC-ROC scores for the ten classes of defects in the DAGM data set, so the performance of our model is better than [PERSON]. Furthermore, Figure 6 presents the ROC curves for the 10 classes of defects in the DAGM test data set. According to the ROC curve, the model achieves good classification performance in all classes except the second and eighth classes. The reason for the not perfect performance of the second and eighth classes of defects may be that their defects and textures are too similar. It should be noted that far fewer positive samples exist than negative samples in each class of DAGM dataset and that the proportion of positive and negative samples is approximately 1:7. So it can be concluded that our model performs well on ROC and can perform well even when positive and negative samples are unbalanced, benefitting from our asymmetric loss function. TPR/TNR Evaluation. We calculate the TPR/TNR scores of defect detection results of our network, [PERSON]'s [18] and [PERSON]'s [20]. As shown in Table 4, we detect all 10 classes of defects in DAGM dataset, whereas other methods only performed defect detection for six classes. The experimental results show that our method get the highest TPR/TNR mean values, which means that the detect results of our method have best detect performance in this three methods. The reason why TPR is slightly lower than TNR might be that texture of the sample is irregular, and selected positive samples with defects are mistaken for defect-free. Figure 5: The surface defect detection result of our model. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Class** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **Mean** \\ \hline
**Benjamin [21]** & 0.98 & 0.47 & 1 & 0.49 & 1 & 0.98 & 0.95 & 1 & 0.98 & 0.57 & 0.83 \\ \hline
**Ours** & 1 & 0.94 & 1 & 1 & 1 & 1 & 1 & 0.99 & 1 & 1 & **0.99** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Area under the receiver-operating characteristic (AUC-ROC) curve performance comparison, higher mean is better. Parameters Comparison. In practical applications, network models with too many parameters are difficult to use in resource-constrained hardware. In order to prove that our model is more convenient to apply in practical, we compared the parameters of our network to the three networks mentioned above. The experimental results are shown in Table 5. The parameters of the convolution filter in our model and other three methods are 2.12M, 6.07M, 6.98M, 135.59M. This result shows that our model has the fewest parameters, thus it is more convenient to apply on hardware with limited resources. We tested the speed of our model on small devices, and the detection rate can reach 24 ms per image on the NVIDIA Jetson Nano platform, which can meet industrial demands.