# Ceramic Cracks Segmentation with Deep Learning
## 5 Results and Discussions  
The models were selected according to their success in the segmentation of images, as previously presented. After that, 20 fine-tuning experiments were performed, altering the models' backbone and the weight initialization. Table 1 outlines each model's three best results, utilizing the IoU metric. In terms of average IoU, our model CCS overcame the IoU value from the other models. We obtain an index of 86.5% in CCS model, and in the U-Net and LinkNet models, the value is around 68%. The obtained average precision in our approach in the U-Net model reaches a value of 99.9%. The U-Net and LinkNet models demonstrate that the resnet50 and resnet34 models, when initialized with the ImageNet weights, present an average precision of 72.7%. Regarding the average recall, to U-Net with resnet50 backbone, without weight initialization, and LinkNet with resnet34, initialized with the ImageNet weights, obtained percentages were equal to 94.6%. This value overcomes the average recall of our approach, with a value of 78.7%. Our approach shows a high value of accuracy and a low recall. However, most of our predicted labels are correct. The best average kappa coefficient is obtained by a LinkNet model with a resnet34 backbone, using the ImageNet weights, with 81.4%. All the models to U-Net and LinkNet achieve a value of kappa coefficient around 80%. The lower value is from our approach, with 72.4%, followed by the LinkNet model with vgg16 and ImageNet initialization. We observe that the models with vgg16 architecture reach the low values of the kappa coefficient. The average value of specificity for the U-Net and LinkNet models reached a value of around 98%. Our approach obtains a value of 99.9%, overcoming the other values; nonetheless, it does not seem to show a significant difference. Regarding the confusion matrix, our approach correctly classified 99% of the pixels that belong to the crack (Figure 6a). However, 27% of the pixels that are not from the crack are classified as part of it. Using the U-Net model, resnet50 with the random initialization, 95% of the pixels are correctly classified as cracks (Figure 6b). For all other implementations (Figure 6c,d), 93% of the crack is correctly classified. The Linknet model, with resnet34 and initialization with ImageNet, correctly classify 95% of the crack pixels (Figure 6e), surpassing the other implementations (Figure 6f,g) with values of 90% and 84%. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{6}{c}{**Models**} \\ \cline{2-8}  & \multicolumn{3}{c}{**U-Net**} & \multicolumn{3}{c}{**LinkNet**} \\ \cline{2-8}  & **CCS \(\uparrow\)** & **resnet50 \(\uparrow\)** & **resnet50** & **resnet34** & **resnet34 \(\uparrow\)** & **vgg16** \\ \hline IoU & 0.865 & 0.685 & 0.681 & 0.675 & 0.697 & 0.684 & 0.672 \\ Precision & 0.999 & 0.713 & 0.727 & 0.720 & 0.727 & 0.711 & 0.704 \\ Recall & 0787 & 0.946 & 0.933 & 0.929 & 0.946 & 0.922 & 0.897 \\ Kappa & 0.724 & 0.805 & 0.808 & 0.803 & 0.814 & 0.794 & 0.775 \\ Specificity & 0.999 & 0.988 & 0.988 & 0.988 & 0.988 & 0.987 & 0.988 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Result of the metrics for the U-Net and LinkNet models, and the different network architectures. The weights of a pre-trained network with ImageNet are used to initialize each of the models. Qualitative analysis can be illustrated with the results presented in Figure 7. The Figure shows an example image (Figure 7a) and its ground truth (Figure 7b). The original image is inputted to the network, having the expected output (Figure 7c-i). It is possible to observe mistakes in segmentation labeling in some regions. Figure 7c,h shows a thick segmentation in comparison with the other predictions, overestimating the region where the crack is. A fine segmentation can lose crack representation. In Figure 7e, some parts of the crack are not identified. It should be noted that the models above can segment the cracks that are above or near the grouts.