## 1 Introduction  
The cleaning and maintenance of large facades, such as ships and tanks, is a high-risk industry, and currently, the rust removal and painting of large facades are mostly carried out by hand with high-pressure water guns and spray guns [1; 2]. There are many drawbacks to manual work, the most prominent of which is safety. The person operating the high-pressure water gun at heights runs the risk of falling. In addition, large amounts of toluene in the paint can cause serious damage to the operator's health. Wall-climbing robots are therefore beginning to gradually replace risky manual work at heights. The use of wall-climbing robots reduces the risk of manual work to some extent. In addition to improving safety, they can also increase task efficiency and reduce the labor costs. [PERSON] et al. reviewed the characteristics, research fields and designs of wall-climbing robots in the last decade, and many institutions have studied designs of mechanical structures to improve wall-climbing robots [3]. Wall-climbing robots have been developed using negative pressure adsorption, magnetic adsorption and bionic adsorption, all of which have good climbing capabilities. They have also been designed to climb and move on corresponding large facades in complex environments, such as ships and oil tanks. Developments in convolutional neural networks and image-processing technologies have driven the automated application of wall-climbing robots, making machine vision increasingly important [4; 5]. The application of machine vision not only enables efficient and highly accurate product manufacturing in factory production but also allows robots to replace humans in hazardous environments for inspection and operational tasks. Surface corrosion inspection and quality inspection problems for large facades, such as ships and storage tanks, are also increasingly being addressed by wall-climbing robots, and much research has been carried out regarding this problem. This includes the mechanical design of robots to work in complex environments, such as ships and tanks; inspection systems to enable autonomous robotic inspection; and rust removal inspection algorithms to solve facade operation problems. Real-time ranging based on binocular vision has also been the focus of computer vision development in recent years. With the development of computer vision theory, binocular stereo-vision measurement is playing an increasingly important role in industrial measurement [6]. The accuracy of matching a robot's operating parameters depends on the accuracy of the ranging, so this paper has requirements for ranging accuracy. In dimensional calculations, the binocular camera eliminates scale uncertainty compared to the monocular camera, allowing for more accurate dimensional calculations. Therefore, binocular vision is chosen to implement the recognition and ranging functions in this paper. In order to solve the problem of automated operation and effect inspection for large facade cleaning and the maintenance of ships and storage tanks, this paper proposes a method based on binocular vision that can operate on facade protrusions and can assist wall-climbing robots in autonomous rust removal and painting. Combined with binocular vision, a closed-loop feedback intelligent operation of protrusions recognition, dimension parameter matching, process parameter matching, real-time operation effect inspection and the dynamic adjustment of the operation parameters can be achieved. The main contributions of this research are as follows:  
* A method of operating on facade protrusions is proposed in combination with a database that provides a parameter reference for facade operation and increases the reliability and adaptability of the operation on different facade protrusions. * Based on the results of the operational effect inspection, a solution for the adjustment of operational parameters is proposed, which enables dynamic parameter adjustment and ensures the quality of the facade operation. The rest of the paper is organized as follows.