## 5 Results and Discussion  
In this section, we describe the evaluation of all the models mentioned in Table 2 on the dataset we developed. In this study, 9600 images were randomly selected as the training set, and the other 800 images were used as the validation set. As mentioned in Section 4, all the models were divided into four groups depending on their parameters, and models within the same group were used for comparative studies. We chose this method because the application scenarios of the models with different numbers of parameters are different. Lightweight models can be deployed on devices with lower computing power such as smartphones, while large models require the support of GPUs with higher computing power. The models in Group 1 are fairly lightweight models, and their results are listed in Table 3. SSDlite320 MobileNetV3-Large is a model based on SSD and MobileNet, and the input images needed to be resized to 320 \(\times\) 320 pixels. Its MAC value was much lower than that of YOLOv5n and YOLOv5s, which means that its computational complexity was very low. Except for the computational complexity, this model lagged behind for all other metrics across the board. It is also interesting that despite having the lowest computational complexity, the training time and inference time of SSDlite320 MobileNetV3-Large were longer than those of the other two models. From the confusion matrix (see Figure 7), 4476 detected instances containing only the background were mistakenly considered as cracks and sealed cracks by SSDlite320 MobileNetV3-Large, but for YOLOv5n and YOLOv5s, the numbers of mistaken backgrounds were 3399 and 2771, respectively. In Table 3, we can see that the results of YOLOv5n and YOLOv5s are very close, but the computational complexity of the former is only about a quarter of the latter. Their PR curves and confusion matrices are shown in Figure 7.  
In the second group, Faster R-CNN MobileNetV3-Large-FPN and Faster R-CNN MobileNetV3-Large-320-FPN are based on Faster R-CNN and MobileNet. The difference is that the input of the latter is resized to 320 \(\times\) 320 pixels. Although the parameters of the two networks are exactly the same, the computational complexity of the high-resolution model is much smaller than the other networks. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Training Time** & **Inference Time** & **Precision** & **Recall** & **F1** & **mAP (COCO)** & **(Pascal VOC)** & **MACs** \\ \hline SSDlite320 MobileNetV3-Large & 230 s & 40.2 ms & 65.81\% & 56.07\% & 60.55\% & 14.62\% & 45.57\% & 528.96 M \\ YOLOv5n & 51 s & 14.8 ms & 80.18\% & 89.50\% & 84.58\% & 50.50\% & 88.52\% & 2.235 G \\ YOLOv5s & 52 s & 14.8 ms & 83.38\% & 90.45\% & 86.79\% & 55.80\% & 88.04\% & 8.218 G \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of the models in Group 1. Figure 7: PR curves and confusion matrices of models in Group 1: **(a)** PR curve of SSDlite320 MobileNetV3-Large; **(b)** confusion matrix of SSDlite320 MobileNetV3-Large; **(c)** PR curve of YOLOv5n; **(d)** confusion matrix of YOLOv5n; **(e)** PR curve of YOLOv5s; **(f)** confusion matrix of YOLOv5s. was seven times that of the low-resolution model. It can be seen from the results in Table 4 that the low-resolution version was completely backward. Considering the results of the SSDlite320 MobileNetV3-Large in the first group, we argue that scaling down the image size leads to poor performance, since the dataset developed in this study can be considered to be a small object dataset, and reducing the resolution means loss of information. The parameters of YOLOv5m are close to those of the previous two networks, but the results were comprehensively better. The PR curves and confusion matrices are shown in Figure 8.