# Concrete Crack Identification and Image Mosaic Based on Image Processing
## 3 Experimental Validation
### Discussion and Future Work  
It can be seen from the calculation results of the crack width that the calculation accuracy of the two methods increased as the crack width increased. However, as the crack width decreased, the trends of the accuracies of the two methods differed. The accuracy of the optical similarity method represented by the object distance method was much higher than that of the scale conversion method represented by the scale method, possibly because the calibration plate inevitably had a certain error, and the square border of the calibration plate itself occupied a certain width. When the crack width was small, the error caused by this width could not be ignored. However, there was no such problem with optical similarity rules. In general, for the proposed crack length calculation method, the calculation accuracy should increase as the crack length increases when the crack quality is good. However, this rule was not shown in this experiment because the calculation of the crack length was based on image processing technology. The image processing technology was not only related to the actual length of the crack but also to the clarity of the image, the influence of noise, the width and shape of the crack, and other factors. Due to these factors, when the number of cracks was small, the calculation results showed no regularity. Additionally, by improving the splicing program and verifying the splicing accuracy, the splicing effect was quantified in this paper, which could directly reflect the development of the cracks.  
In recent years, crack detection methods based on UAVs have attracted widespread attention due to their stability and high efficiency. The cloud platform of the UAV can ensure that the camera is completely horizontal during the shooting process. A drone image acquisition system equipped with a laser range finder can simultaneously acquire the crack image and the corresponding focal length and working distance in the front view of the camera. However, to obtain a complete crack, the use of the object distance method requires the controlling of the flying speed of the UAV and the shooting time interval, which is undoubtedly inefficient. Therefore, in the future, the range finder should be replaced with two laser emitters with a pitch of 10 cm mounted on both sides of the camera so that the emitted laser light is always within the shooting range. Then, the UAV can calculate the crack size using a video by taking the emitted laser as a ruler, which will greatly improve the detection efficiency. Since a laser spot has a certain size, improving measurement accuracy is still a difficult aspect of crack detection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Crack Number** & **Pixel Distance** & **Real Distance (mm)** & **Mean Error (\%)** \\ \hline \multirow{4}{*}{(a)} & 270.57 & 62.5 & \multirow{4}{*}{\(1.1\)} \\  & 325.45 & 74.0 & \\  & 596.38 & 136.5 & \\ \hline \multirow{4}{*}{(b)} & 403.51 & 58.5 & \multirow{4}{*}{\(0.51\)} \\  & 495.37 & 71.5 & \\  & 1098.94 & 160.0 & \\ \hline \multirow{4}{*}{(c)} & 230.61 & 42.5 & \multirow{4}{*}{\(1.1\)} \\  & 432.55 & 81.0 & \\  & 579.61 & 107.0 & \\ \hline \multirow{4}{*}{(d)} & 342.40 & 52.0 & \multirow{4}{*}{\(2.0\)} \\  & 318.07 & 47.5 & \\ \cline{1-1}  & 647.61 & 99.5 & \\ \hline \multirow{4}{*}{(e)} & 1365.14 & 94.5 & \multirow{4}{*}{\(1.1\)} \\  & 769.21 & 54.5 & \\ \cline{1-1}  & 607.78 & 41.5 & \\ \cline{1-1}  & 696.70 & 47.5 & \\ \cline{1-1}  & 1293.65 & 87.5 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy validation of image stitching.