## 3 Experimental Results  
To predict the defect shape using the MONDI system, the classifier requires a deeper understanding of the visible defect features. The MONDI system has successfully shown a high sensitivity in defect observation and precise transcription for complex crack geometry, which is very beneficial for the feature extraction process in CNNs. MO images seem to look different from the true defect shape because instead of imaging the physical shape, the polarizing optical system captures the rotation intensity of the light plane corresponding to the Faraday rotation effect represented by the image intensity on each pixel, thereby very beneficial for the feature extraction process on CNN. The main goal of implementing the transfer learning method is to correctly predict the shape of a defect material during a surface inspection using the MONDI system. From the 4752 image datasets captured during the experiment, 60% of the dataset was allocated as the training dataset, and 20% was allocated as the validation dataset. Then, the last 20% was stored separately as the testing dataset, i.e., to evaluate the network performance after finishing the training. The training and validation datasets were transformed in the augmentation layer via a random translation to prevent overfitting during training. The convolution neural network was trained on MATLAB R2021a using five pretrained architectures: AlexNet, GoogLeNet, VGG16, ResNet50, and InceptionV3. As mentioned above, this study compared three variations of image preprocessing (the raw cut dataset, CLAHE dataset, and extraction dataset) to evaluate the performance and efficiency of each transfer learning model, majority voting model, and ensemble model (Figure 7). All of the computation processes, training, validating, testing, and network analysis were performed using a Dell ALIENWARE PC with an Intel(r) Core(tm) i9-9900 HQ 3.6 GHz of CPU and 32 GB of RAM. The model was trained with a 3 \(\times\) 10\({}^{-4}\) learning rate and 150 maximum epochs. The early stop training method was applied to obtain the best parameter performance and prevent overfitting during training. The patience value for the early stop method was set at 25, meaning that if the validation loss did not decrease within 25 epochs, the training process would stop automatically. The computer was turned off for two hours after finishing the training to subside the system heat before continuing with the following models, to prevent uncertain results owing to a drop in computation performance. In particular, a performance drop could occur during training because of thermal throttling, causing the GPU or CPU to decrease the clock speed linearly with the increasing temperature. Several positive results were obtained in this study by successfully preventing overfitting, underfitting, and performance drops during the training, thereby ensuring the validity of the testing results for further research. Figure 8 compares the network performance with the average training variance and training and validation accuracy plots during the network training using the transfer learning approach in the deep CNN. The training process was completed with the 15 network models without indicating either overfitting or underfitting, by maintaining an average variance between the training accuracy and validation accuracy of approximately 4.98 \(\pm\) 0.4%. Overfitting is a condition in which the training and validation performance have a low bias and high variance, whereas underfitting is when they have a high bias and low variance. The average testing accuracy of each transfer learning model gave a relatively higher performance than the validation accuracy during network testing, indicating that the testing dataset was easier to predict. Figure 7: Three variation datasets: raw cut image, CLAHE image, and extraction image.