## 4 Results and Discussion
### Crack Identification and Measurement  
We can input the above-processed image data into the pre-trained convolutional neural network in the previous chapter, and then use the pre-trained parameters to perform recognition work to determine whether the input image contains cracks. If any image in the folder with the same place name is identified as having a crack, the entire engineering facility is considered cracked. Finally, the photos are divided into two cases: cracks and no cracks. After the convolutional neural network screening process, 146 crack photos were identified.  
The elevation information and phase information provided by integrating PS-InSAR technology and DEM data can quickly screen out possible settlement points, which solves the problem of slow and incomplete detection of settlement phenomena in the past by manual inspection. In this case study, we used the technical framework proposed in the previous paper to find out the multiple settlement points in Shenzhen University and then carried out an artificial investigation of these abnormal areas. The results showed that different degrees of the settlement were found in the abnormal areas forming settlement cracks, which successfully verified the effectiveness of the settlement screening model. In addition, it took less than one hour from the beginning of finding the abnormal point to the final arrival at the site for confirmation, which greatly sped up the efficiency of settlement detection.  
After identifying the areas of concern, we employed drones to capture vertical-angle images of the cracks in subsided structures, thereby preventing distortion of crack images. Detailed shooting information was also documented. These images will be input into the crack recognition model trained after a series of preprocessing. In order to make the model more versatile, we used crack images from different materials and different engineering facilities to train the model. The results show that after the new image is input into the model, the accuracy of the model is as high as 98.48% in the classification of whether there are cracks, which is much higher than the accuracy of the human eye to distinguish cracks.  
Figure 9: UAV route map. The yellow numbers 1–11 in the figure are the 11 hazardous and unusual facilities.  
Among them, some are cracks in the exterior wall of high-rise buildings with a width of less than 7 mm, which are almost impossible to find via manual observation and cannot be quantified in high-rise buildings.  
After the fracture was found in the fracture recognition model, the corresponding region of the fractured image was classified into the abnormal region and used as the input of the fracture analysis model to further extract the fracture information. As shown in Figure 10i, a total of 38 cracks of different sizes were found in this area, and the specific information of the cracks was output as a text document (Figure 10)), which was stored together with the processed image. The length of the longest crack is 5.7 m, and the width is less than 1 cm. This crack information helps to evaluate the damage degree to engineering facilities further. Table 6 shows the results of the crack analysis.  
Figure 10: Crack monitoring process: (**a**) a crack image taken by drone; (**b**) rectangle cropping images; (**c**) VGG16 network architecture; (**d**) images after classified; (**e**) an image that contains a crack; (**f**) the  
crack image after grayscaling; (**g**) the crack image after image enhancement; (**h**) the crack image after morphological processing; (**i**) the overlaying crack image; (**j**) the output information of crack.