# Fatigue Crack Detection Based on Semantic Segmentation Using DeepLabV3+ for Steel Girder Bridges
## 4 Comparative Studies of Different Models  
This section describes the comparison results of different models. In order to verify the effectiveness of the improved strategy proposed in this paper, we conducted comparative experiments based on the Deeplabv3+ algorithm. The first and second models are the DeeplabV3+ with ResNet50 and mobilenet-v2 [15] as backbone networks. They are compared with the proposed one. It should be noted that the difference between the second network and the proposed one is that the proposed one is based on ResNet50, which is re-tuned as a classification network using a crack detection dataset. Every model was trained for 20 epochs.  
This comparison is carried out on the test dataset, which contains 20 raw pictures. All investigations are performed on a Lenovo workstation P910, which is installed with Nvidia GeForce, Santa Clara, CA, USA, GTX 1080 Ti, and MATLAB 2020a.  
Figure 4: The training process of the semantic segmentation network DeepLabv3+.  
The training results are shown in Table 1. The lowest training accuracy is seen in mobilenet-v2 and is 99%, and the highest is ResNet50_crack, which is 99.26%. Compared with mobileNet-v2, ResNet50_crack and ResNet50 are deeper and then have stronger generalization ability for samples with complex backgrounds and finer cracks. Compared with ResNet50, ResNet50_crack has undergone crack classification pre-training, and thus the training of the semantic segmentation network is easier and has higher accuracy. Table 1 shows that the training process is slow but acceptable.  
1. Comparison of semantic segmentation indicators  
In order to measure the role and contribution of the segmentation system, its performance needs to be rigorously evaluated. Moreover, the evaluation must use standard and recognized methods to ensure fairness. Therefore, the commonly used indicators of semantic segmentation usually include: accuracy, intersection ratio (IoU), and average intersection ratio (MIoU).  
The performance indicators of three models for the test dataset are summarized in Table 2. Clearly, the semantic segmentation network based on MobileNet-v2 performs the worst in terms of IoU for crack pixels and MIoU for the model. Conversely, the proposed model outperforms the others, which can be attributed to re-tuning the classification network ResNet50 and active learning.  
The crack distribution predicted by Model 1 and the proposed model are compared in Figure 5 together with the ground truth. Clearly, the proposed model can provide results that are more consistent with the ground truth.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network Type** & **Time (h)** & **Training Accuracy of Crack Pixels** & **Training Accuracy of Background Pixels** & **Global Accuracy** & **Validation Accuracy** \\ \hline Model 1: & & & & \\ DeeplabV3+, ResNet50 & 18.9 & 78.52\% & 99.74\% & 99.71\% & 99.11\% \\ \hline Model 2: & & & & \\ DeeplabV3+, MobileNet-v2 & 14.7 & 70.09\% & 99.67\% & 99.64\% & 99.00\% \\ \hline Proposed Model: & & & & \\ DeeplabV3+, ResNet50\_crack, Active Learning & 12.25 & 75.59\% & 99.89\% & 99.86\% & 99.26\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics for network training.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Type** & \multicolumn{3}{c}{**IoU**} \\ \cline{2-4}  & **Crack** & **Background** & **MIoU** \\ \hline Model 1: & & & \\ DeeplabV3+, ResNet50 & 0.2391 & 0.9971 & 0.6181 \\ \hline Model 2: & & & \\ DeeplabV3+, MobileNet-v2 & 0.1910 & 0.9964 & 0.5937 \\ \hline Proposed Model: & & & \\ DeeplabV3+, ResNet50\_crack, Active Learning & 0.3897 & 0.9986 & 0.6942 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Semantic segmentation indicators for the test dataset.