## 4 Experimental Results
### Performance on Disturbance Defect Dataset  
#### 4.5.1 Recognition Results on the Disturbance Defect Dataset  
In the real-world manufacturing process, the images captured may not be the same as in the public dataset. For example, low luminance, different viewpoint problems, and inevitable factors (i.e., vibration and white noise) will directly influence the quality of the images. To further evaluate the robustness of the proposed MRFFN, the disturbance defect dataset is utilized in this experiment as the training and testing set. Hence, this section will discuss the robustness of the proposed method with three common interference conditions: motion blur, Gaussian white noise, and salt and pepper noise. This discussion will be separated into two parts: (a) the improvement of the proposed method, which is trained by the interference defect dataset, and (b) a comparison of the results between the conventional pre-trained DL models and the MRFFN. Based on the results below, the proposed method trained by the original dataset underperformed on the interference dataset. However, while retraining the model with the noise input samples, the proposed method consistently outperformed most conditions. According to Tables 4 and 5, MRFFN achieved a remarkable performance, with an accuracy of over 90% on Gaussian white noise and salt and pepper noise tasks. The accuracy gaps between each experiment were about 1% to 3%. However, the proposed method was slightly inferior once the variance and density reached 0.3. The accuracy gaps between variance 0.1 and variance 0.3, and density 0.1 and density 0.3 increased significantly, implying that the inspection tasks become more arduous when the disturbance factors become higher. Nonetheless, the proposed method works well in motion blur tasks, as shown by the results in Table 6. In these results, it can be seen that MRFFN retains 96.33% accuracy even the motion length comes to 20 and the accuracy gaps between each experiment are about 1%. Lastly, MRFFN with image augmentation promoted the accuracies of the MRFFN, especially for high noises and motion blur tasks. For instance, the performance of MRFFN increased by 2.6% in variance 0.3 task, 3.45% in density 0.3 task, and 1.03% on motion length 20 tasks. In addition, to visualize the classification performance on the disturbance defect dataset, a nonlinear dimensionality reduction technique, namely t-distributed stochastic neighbor embedding (t-SNE), was adopted to visualize the classification performance on the disturbance defect dataset. Concretely, t-SNE reduced the data dimensionality so that it was easier to interpret and analyze. Besides, the proposed MRFFN adopted multiple inception modules to extract multiscale features and applied the max pooling layer to downsample the aggregate features. Lastly, a fully connected layer was applied to extract all of the discriminative information from the above layers. Hence, the activations of the fully connected layer from both the level 0 and level 1 networks were applied as the feature vectors of the feature visualization to provide a better interpretation of the activations for the decision of interest. Figures 7 and 8 demonstrate the data distribution of the proposed \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Cr** & **In** & **Pa** & **Ps** & **Rs** & **Sc** & **Precision** \\ \hline
**Cr** & **100.00** & 0.00 & 0.00 & 0.24 & 0.00 & 0.00 & 99.76 \\
**In** & 0.00 & **99.20** & 0.00 & 0.20 & 0.00 & 0.28 & 99.52 \\
**Pa** & 0.00 & 0.00 & **100.00** & 0.00 & 0.00 & 0.00 & 100.00 \\
**Ps** & 0.00 & 0.72 & 0.00 & **99.56** & 0.00 & 0.00 & 99.28 \\
**Rs** & 0.00 & 0.08 & 0.00 & 0.00 & **100.00** & 0.00 & 99.92 \\
**Sc** & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & **99.72** & 100.00 \\
**Recall** & 100.0 & 99.2 & 100.0 & 99.6 & 100.0 & 99.7 & **99.75** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The confusion matrix of MRFFN + IA based on the NEU dataset (%). MRFFN on Gaussian white noise and salt and pepper noise datasets.