## 4 Recognition and Operation of Facade Protrusions
### Matching of Process Parameters
A total of 51 images were captured by the binocular vision inspection system and the comparison value for each image was displayed in the top left corner, with some of the inspection results shown in \[d(H_{1},H_{2})=\Sigma_{l}\frac{\left(H_{1}(I)-H_{2}(I)\right)^{2}}{H_{1}(I)+H_ {2}(I)} \tag{1}\]1

The inspection of evaluation indicators obtained is shown in \[d(H_{1},H_{2})=\Sigma_{l}\frac{\left(H_{1}(I)-H_{2}(I)\right)^{2}}{H_{1}(I)+H_ {2}(I)} \tag{1}\]2 The data show that the rust removal effect inspection has an accuracy rate of 98%, while the accuracy rate is only 93.33%. This is because the binocular inspection system lost edge information when processing the images, inspecting the pictures that were actually unqualified for rust removal as qualified, resulting in a low accuracy rate. In order to avoid this problem, the images of the facade surface can be fully captured by controlling the motion path of the wall-climbing robot in the actual work process. \[d(H_{1},H_{2})=\Sigma_{l}\frac{\left(H_{1}(I)-H_{2}(I)\right)^{2}}{H_{1}(I)+H_ {2}(I)} \tag{1}\]3 Inspection under different rust removal effects. \[d(H_{1},H_{2})=\Sigma_{l}\frac{\left(H_{1}(I)-H_{2}(I)\right)^{2}}{H_{1}(I)+H_ {2}(I)} \tag{1}\]4 Comparison of complete and incomplete rust removal images: (**a**) complete rust removal image; (**b**) incomplete rust removal image. 2. Painting inspection

After simulating a painting operation scene on the facade, the binocular vision inspection system acquired 12 images of the missed painting area, using Canny edge detection to extract the curves on both sides of the missed painting area, and then measured the actual maximum distance of the missed painting area. As shown in Figure 11, the Canny edge detection clearly extracts the outline of the missed painting area, providing pixel distance information for the subsequent calculation of the actual distance. On the basis of the edge image obtained by Canny edge detection, the distance of the line pixel coordinates on all the same line of the edge was calculated iteratively, then the maximum line pixel coordinate distance was obtained by comparing each line, and finally, it was multiplied by the pixel equivalent to obtain the actual distance of the missed painting area. After the experiment, the actual missed painting distance of 100 mm was measured in 12 groups, and the experimental data are recorded in Table 7 and the error curve plotted is shown in \[d(H_{1},H_{2})=\Sigma_{l}\frac{\left(H_{1}(I)-H_{2}(I)\right)^{2}}{H_{1}(I)+H_ {2}(I)} \tag{1}\]5 \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Precision (\%)** & **Accuracy (\%)** & **Recall (\%)** & **Specificity (\%)** & **G-Mean** \\ \hline
98.04 & 93.33 & 100 & 97.30 & 0.986 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance evaluation indicators of rust removal effect inspection. Figure 11: Process of inspecting missed painted areas: (**a**) image of painting to be tested; (**b**) Canny edge detection result. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Actual Distance** & **Measuring Distance** & **Absolute Error** & **Relative Error** \\ \hline
1 & 100 & 97.51 & 2.49 & 2.49\% \\
2 & 100 & 99.07 & 0.93 & 0.93\% \\
3 & 100 & 99.84 & 0.16 & 0.16\% \\
4 & 100 & 98.68 & 1.32 & 1.32\% \\
5 & 100 & 99.07 & 0.93 & 0.93\% \\
6 & 100 & 98.68 & 1.32 & 1.32\% \\
7 & 100 & 101.01 & 1.01 & 1.01\% \\
8 & 100 & 99.46 & 0.54 & 0.54\% \\
9 & 100 & 100.52 & 0.52 & 0.52\% \\
10 & 100 & 100.04 & 0.04 & 0.04\% \\
11 & 100 & 99.13 & 0.87 & 0.87\% \\
12 & 100 & 99.01 & 0.99 & 0.99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Measurement results of missed painting distances. According to Figure 12, the absolute error of the binocular inspection system for the measurement of the missed painting distance is less than 3 mm, and the average relative error is less than 2%, which meets actual engineering requirements.