## 4 Recognition and Operation of Facade Protrusions
### Matching of Process Parameters
During the operation of the system, data transmission between devices adopts protocols with low energy consumption and latency [35]. Before the camera can be used for recognition, the calibration and correction of the binocular camera are required. The internal and external parameters of the camera are calibrated and solved using corner point detection, the aberration parameters are obtained using least squares and, finally, the Bouguet algorithm is used to achieve stereo correction. ### Fagade Protrusion Recognition

1. Classification

The recognition algorithm uses the YOLOv5 convolutional neural network algorithm. After collecting 1000 samples, the samples were trained and the training parameters were adjusted. After 500 training sessions, the accuracy of the model was evaluated with an average accuracy of 99.63%. The recognition accuracy of the model was tested under different weather conditions, and the recognition results included the category of the target object, the candidate frame, the coordinate value and the confidence level. The recognition results are shown in Figure 7. The confidence level of the model in recognizing cylindrical objects under different lighting conditions and at different orientations exceeded 0.9. Figure 6: Experimental platform construction: (**a**) experimental prototype in a simulated facade environment; (**b**) hardware components of the experimental prototype. Figure 7: Recognition results in different weather conditions and orientations. Distance measurement

The SGBM algorithm was applied to stereo matching to obtain depth images. After correcting and matching the image of the 800 mm column section, a depth image could be obtained, as shown in Figure 8, and the experimentally measured depths were shown in Table 5.

Table 5 shows that the depth error increases as the distance increases. Of the depth error at 1000 mm, 1.579% is sufficient to meet operational requirements. ### Operation Effect Inspection

1. Rust removal inspection

A total of 200 pictures were taken to simulate facade operation scenes with different degrees of rust removal. In accordance with the technical acceptance index for rust removal on large Chinese ships, such as the Sa2.5 grade in GB 8923, the individual pictures were assessed by comparing the sample pictures, as shown in Figure 9. The samples were divided into two categories--qualified and unqualified. Of the samples, there were 42 pictures in the qualified category and 158 pictures in the unqualified category. The images of complete rust removal were compared with 200 samples and the comparison value was recorded. The results showed that the comparison values of the images of complete rust removal were all less than 1, and the comparison values of the images of incomplete rust removal were all greater than 1.5. Based on these results, the threshold value of comparison was set at 1.2. The rust removal was complete when the comparison value was less than or equal to the threshold value, and the rust removal was incomplete when the comparison value was greater than the threshold value. \[d(H_{1},H_{2})=\Sigma_{l}\frac{\left(H_{1}(I)-H_{2}(I)\right)^{2}}{H_{1}(I)+H_ {2}(I)} \tag{1}\]0 Depth image of a columnar target. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. ** & **Actual Depth/mm** & **Measuring Depth/mm** & **Absolute Error/mm** & **Relative Error** \\ \hline
1 & 600 & 587.42 & 12.58 & 2.097\% \\
2 & 700 & 689.42 & 10.58 & 1.511\% \\
3 & 800 & 785.35 & 14.65 & 1.831\% \\
4 & 900 & 886.77 & 13.23 & 1.4



After defining the threshold value of comparison, the inspection algorithm was applied to the wall-climbing robot to inspect the effect of rust removal.