## 4 Recognition and Operation of Facade Protrusions
### Matching of Process Parameters
## 7 Conclusions and Future Works

This paper proposes a binocular vision-based method for rust removal and painting on the large-scale facades of ships and tanks to address the problems of cleaning and maintenance. We designed a three-part process that included detection, operation and feedback adjustment on facades. The results of the visual recognition are compared with the dimension database to increase the reliability of the recognition. Different operation parameters were used according to the process database for different protrusions, enabling the robot to take appropriate operations on protrusions in the environment with different protrusions. Based on image processing technology to inspect the operation effect, the robot receives the inspection results and decides whether to adjust the operation parameters, which realizes the dynamic parameter adjustment and improves the operation effect. Experimental results show that the accuracy of facade protrusion recognition reaches 99.63% and the accuracy of the rust removal effect inspection reaches 93.33%. The absolute error of the missed painting distance measurement is less than 3 mm, and the average relative error is less than 2%. As mentioned in related works, no similar application has been found in the field of assisting wall-climbing robots in recognizing facade protrusion. However, by searching for the application of visual recognition in non-facade operations, such as pipes and ship cabins, and comparing it with the method proposed in this article, it has the same level of accuracy. In addition, compared with the recognition accuracy of the rust removal effect in the field of facade operations, the inspection accuracy of the method proposed in this article is also within the normal range, which meets the actual operational requirements of wall-climbing robots. However, the innovation of this paper is that it is possible to accomplish both facade protrusion recognition and operation effect inspection with one vision system, which has the advantage of more functions and higher integration. There are still some shortcomings in the proposed method; for example, the computation of the missed painting distance measurement is large when inspecting missed painting with complex irregular shapes; since the operation parameters depend on the result of facade protrusion recognition, it takes much time for the robot to adjust operation parameters when the result of recognition is wrong. In future research, depending on the relevant problems in future experiments, the following directions could be investigated:

Figure 12: Measurement error curve of missed painting distance. 1. The algorithm of missed painting distance measurement could be improved in order to reduce the computation. 2. A method could be designed to remedy the errors in facade protrusion recognition, which would improve the fault tolerance of robot operation. 3. The vision detection system proposed in this paper could be combined with path planning to improve the intelligence of a wall-climbing robot on a facade. Conceptualization and administration, M.Z. ; writing, Y.M. and Z.L. ; data sources, J.H. ; review and editing, Y.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key R&D Program of China (Grant number 2018 YFB1309400). Not applicable. The data that support the findings of this study are available from the author [PERSON] upon reasonable request. The authors would like to thank [PERSON] and [PERSON] who participated in the experiments. The authors declare no conflict of interest. ## References

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