## 3 Experimental Results
Comparing all network models, it can be seen that the ensemble and majority voting models achieve relatively more balanced prediction results in each dataset, i.e., by successfully reducing the bias from each transfer learning model and proposing more collaborative predictions for  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Epoch**} & \multicolumn{2}{c}{**Testing**} & \multicolumn{2}{c}{**Network**} & \multicolumn{2}{c}{**Weighted**} & \multicolumn{2}{c}{**Area under the**} & \multicolumn{2}{c}{**F1\_Score**} & \multicolumn{2}{c}{**Classification**} \\  & & & **Accuracy** & **Accuracy** & **Accuracy** & **Curve (AUC)** & & & & **Time (s)** \\  & & & (\%) & (\%) & (\%) & (\%) & (\%) & & & & & \\ \hline \multirow{9}{*}{Raw cut Image} & AlexNet & 73 & 93.05 & 97.22 & 97.53 & 97.88 & 0.960 & 0.934 & 0.796 & 0.824 & 0.981 & 2.570 \\  & GoogLeNet & 102 & 93.57 & 97.43 & 97.39 & 98.12 & 0.952 & 0.939 & 0.891 & 0.816 & 0.965 & 2.565 \\  & VGG16 & 150 & 96.42 & 98.57 & 98.70 & 98.70 & 0.975 & 0.972 & 0.901 & 0.901 & 0.987 & 2.570 \\  & ResNet50 & 100 & 90.94 & 96.38 & 97.44 & 97.20 & 0.967 & 0.947 & 0.688 & 0.635 & 0.990 & 2.566 \\  & InceptionV3 & 150 & 93.99 & 97.60 & 88.49 & 98.41 & 0.977 & 0.983 & 0.758 & 0.701 & 0.998 & 2.574 \\  & Majority voting & - & 94.94 & 97.98 & 98.43 & 98.84 & 0.979 & 0.870 & 0.824 & 0.819 & 0.985 & - \\  & Ensemble & - & 95.68 & 98.27 & 98.70 & 98.84 & 0.979 & 0.976 & 0.837 & 0.849 & 0.992 & 2.608 \\ \hline \multirow{9}{*}{Contract- limited adaptive} & AlexNet & 109 & 97.68 & 99.07 & 99.43 & 99.32 & 0.989 & 0.996 & 0.900 & 0.889 & 1 & 2.567 \\  & GoogLeNet & 133 & 97.89 & 99.16 & 99.43 & 99.06 & 0.985 & 0.996 & 0.907 & 0.916 & 1 & 2.565 \\ \cline{1-1}  & VGG16 & 84 & 95.47 & 98.19 & 98.26 & 94.98 & 0.94  
classifying defect shapes. From analyzing model performance on every dataset, the raw cut dataset as performed by the ResNet50 model has a problem predicting the combination and triangle defect classes with the lowest F1 scores of 0.688 and 0.635, respectively. In contrast, the VGG16 model using the CLAHE dataset successfully improves the classification bias by increasing the network performance for the combination and triangle defect classes by 31.4% and 38.5%, respectively. Unfortunately, ambiguous prediction results were detected in the extraction dataset. Thus, it not only failed to improve the prediction performance in the combination and triangle defect classes, but the extraction dataset also indicated a new problem regarding predicting the semioval and triangle defect classes. The weighted F1 score of the weakest extraction model with ResNet50 decreased by 9.3% relative to the CLAHE dataset and by 4.4% relative to the raw cut dataset. However, the training time of the extraction dataset showed a successful and auspicious performance by extracting the image features with an average training time of 28 min and 20 s, improving the training time of the CLAHE and the raw cut dataset by 15% and 21%, respectively. Training and classification time improvements are beneficial in actual industrial inspections, as they commonly involve fast and complex systems. Fortunately, the hole defect class has a relatively consistent performance, as it maintains an F1 score above 0.96 for each dataset and network model. Furthermore, understanding how a network model makes a particular decision is essential to providing check-and-balance control of CNN models and recording model weaknesses for further training. Figure 12 shows a score activation map plotted using occlusion sensitivity, with the aim of further understanding the network behavior [45]. The red area of the score map indicates the area that positively influences the classifier when predicting the defect class. The causes of bias in each dataset were identified using this approach. The bias in the raw cut dataset was caused by an excessive shadow that interfered with the classifier.