## 4 Results  
In our system, a method of distortion correction and post-correction image up-sampling of the visual image with the crack detection in the patch was integrated. The detection data was guided by the ground truth, and the patch was spliced using the confidence mechanism to improve the robustness and efficiency of the system. Finally, combined with the IMU and meter data in the system,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy (\%)** \\ \hline Constant step size learning rate & 92.1 \\ Schedules vary the learning rate & 92.8 \\ Cosine changes the learning rate & 94.2 \\ Stochastic gradient descent optimizer & 94.2 \\ Adams optimizer & 93.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The effects of different training parameters.  
Figure 14: Because distortion mapping and up-sampling are processes that go from coarse to fine, the varying learning rate had a better convergence effect on our system. (**a**) The learning rate of cosine decay is used. (**b**) Constant learning rate.  
the three-dimensional shape of the internal crack was visualized. To verify the effectiveness of the distortion correction, we used the _SSIM_ index to evaluate the crack images from different perspectives after the distortion correction and used a confusion matrix to evaluate the crack recognition. To evaluate the performance of the crack detection, we validated our module on a dataset and achieved a final detection accuracy of 96.3%. Figure 15 shows the effect after 3D visualization.  
For the crack identification part, we constructed an evaluation system to evaluate the robustness of the system. According to whether the extracted area in the patch contained the crack characteristics of corresponding labels, the accuracy of this model was constituted by true-positive (TP), true-negative (TN), false-positive (FP), and false-negative (FN) values according to:  
\[\text{Accuracy}\ =\ \frac{TP+TN}{TP+TN+FP+FN}. \tag{8}\]  
Furthermore, because we have introduced the confidence mechanism to patch, the accuracy was further improved in the actual test. Because the quality of shooting in the actual test process was affected by various factors, we augmented the images with the ground truth during the patch extraction. The experiments showed that our system was efficient and accurate, and greatly reduced the complexity faced by the operators. Table 7 shows the different parameters and indicators, and the comparison between the existing methods and our system. As can be seen from the table, although the traditional mathematical modeling [29] was faster than the deep-learning-based mapping, its accuracy was not high because it could not adapt to most nonlinear distortion relations. If the distortion correction was carried out without the down-sampling operation, we can see that a large amount of processing time was lost, while the accuracy and _SSIM_ exponential increase was not high. By comparing the two models with the same parameters of the patch traversal mechanism without the ground truth and with the ground truth, it can be seen that the accuracy and speed were significantly improved [30; 31]. After adding the confidence guiding mechanism, the recognition speed of the model was further improved.  
Figure 15: The resultant renderings.