# Identification of Underwater Structural Bridge Damage and BIM-Based Bridge Damage Management
## 3 YOLO Algorithm and Point Cloud Algorithm
### Calculation of Concrete Defects Based on 3D Point Cloud Technology  
The emergence of three-dimensional point cloud technology provides technical support for underwater detection. The system adopts a bridge underwater disease measurement algorithm based on a 3D point cloud. Firstly, the calibration camera is used to obtain the 3D point cloud of the underwater disease. The point cloud is filtered and denoised to obtain the length, width, and depth data of the disease. The data are imported into the database, and various disease information can be viewed in the system. Furthermore, the high-order aberration correction polynomial is used to eliminate the underwater image aberration captured by the 3D point cloud camera, completing the underwater calibration of the camera, and determining the correspondence between the 3D points in the underwater space and the image pixels. Finally, the Poisson surface reconstruction algorithm is used to reconstruct the disease, and the Helen formula and 3D convex hull algorithm are used to obtain the area and volume of the pier disease. The specific flowchart is as follows in Figure 7:  
The depth camera can obtain the three-dimensional coordinate values of each point of the damage, calculate the distance between the damage and the camera, and then realise the modelling of the three-dimensional damage in order to grasp the real value of damage [47; 48]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Recall** & **Precision** & **mAP** & **FPS** & **Model Size/MB** &
\begin{tabular}{c} **Training** \\ **Time/Epoch** \\ \end{tabular} \\ \hline YOLO-v4 & 53.61\% & 95.62\% & 84.20\% & 9 & 244 & 15 min \\ \hline CenterNet & 48.53\% & 94.31\% & 77.10\% & 15 & 125 & 8 min \\ \hline YOLO-v4-tiny & 40.03\% & 87.64\% & 66.86\% & 27 & 22.5 & 1.5 min \\ \hline Mobilenetv3-YOLO-v4 & 41.03\% & 91.20\% & 75.13\% & 14 & 152 & 9 min \\ \hline Lite-YOLO-v4 & 47.98\% & 93.97\% & 77.07\% & 25 & 44.3 & 2 min \\ \hline YOLO-V5l & 53.69\% & 92.96\% & 82.49\% & 15 & 178 & 7 min \\ \hline YOLO-V5m & 52.25\% & 89.04\% & 80.41\% & 18 & 81.5 & 4 min \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Values of the Eight Model Indicators Under the Crack Dataset. Figure 7: Three-Dimensional Point Cloud Processing Flow Chart. The three-dimensional image processing of underwater diseases is different from other three-dimensional image processing. The three-dimensional image of underwater diseases has low resolution and is easily affected by water flow and swimming of aquatic organisms, resulting in noise points in the collected point cloud data, which reduces the measurement accuracy. Therefore, statistical filtering denoising is considered. In order to eliminate the influence of adverse factors such as water flow and aquatic organisms and improve the image resolution, the filtering parameters are set in the algorithm to remove the noise point cloud. In order to clearly show the denoising effect, a certain area in the point cloud is selected for detail display, before and after denoising, as shown in Figure 8.  
The denoised point cloud image is divided into three parts, numbered a, b, and c in the order from left to right, as shown in the following Figure 9. This paper uses the concrete disease analysis numbered c as an example of the system calculation process. A nearly flat section surrounds the disease; the pit edge is judged by the gradient change of the Z direction (vertical direction) of the data point. When presetting z direction gradient change threshold, the gradient change of data points less than this threshold is the plane point, and the pit edge point is beyond this threshold. The three-dimensional point cloud of the disease area is obtained by removing the plane points.