## 3 System Overview  
The system is designed to enable the wall-climbing robot to recognize protrusions on the facade, inspect operation effects and adjust parameters during rust removal and painting operations. The system is based on binocular vision, as shown in Figure 1.  
The system consists of two parts: hardware and software.  
The hardware mainly includes the Nvidia Jetson TX2 embedded development board, ZED binocular camera, operation interface, upper computer and mobile platform mechanical body. The ZED camera is fixed onto the chassis support of the climbing robot to ensure stability and is connected to the Nvidia Jetson TX2 through a USB3.0 data cable.  
The software part mainly involves installing the Compute Unified Device Architecture (CUDA), Open-Source Computer Vision Library (OpenCV), My Structured Query Language (MySQL), ZED SDK and a deep learning framework.  
The wall-climbing robot facade operation system mainly includes four functions: (1) database function, (2) recognition and parameter-matching function, (3) effect inspection function and (4) parameter adjustment function. The specific implementation of these four functions is as follows:  
* Database function: The database includes the dimension database and the process database. The dimension database guides the recognition of facade protrusions, and the process database guides rust removal and painting operations.
* Recognition and parameter-matching function: This function realizes the recognition and distance measurement of a facade protrusion and matches the results with the database to obtain dimensions and process parameters to guide robot operation.
* Effect inspection function: After the wall-climbing robot completes the operation on protrusions, it inspects the operation effect.  
Figure 1: Schematic diagram of wall-climbing robot facade operation.  
* Parameter adjustment function: When the inspection result of the operation effect is unqualified, the wall-climbing robot adjusts the corresponding parameters for two different operations--rust removal and painting. The overall system includes visual recognition, the matching of dimensional parameters, the selection of process parameters and real-time inspection of the operation effect. The flow chart of the wall-climbing robot's overall system in this paper is shown in Figure 2.  
The workflow outlined in this article consists mainly of the following steps. Firstly, the dimension database and process database are established, which mainly include the categories, standard dimensions and process parameters of the facade protrusions. Secondly, the wall-climbing robot uses a visual system to recognize and measure the distance of the protrusions on the facade, obtaining the categories and dimensions of the protrusions. Then, the information obtained from the vision is compared with the information in the dimension database to obtain the standard size of the protrusions. This result is then compared with the information in the process database to obtain the corresponding process parameters. After obtaining the process parameters, painting and rust removal operations can be carried out. Finally, the visual system is used to inspect the results of the operation, and the process parameters are adjusted based on whether the results are qualified, thus achieving the automated operation of the protruding objects of the wall-climbing robot.