Façade Protrusion Recognition and Operation-Effect Inspection Methods Based on Binocular Vision for Wall-Climbing Robots

The cleaning and maintenance of large-scale façades is a high-risk industry. Although existing wall-climbing robots can replace humans who work on façade surfaces, it is difficult for them to operate on façade protrusions due to a lack of perception of the surrounding environment. To address this pr...

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Bibliographic Details
Published inApplied sciences Vol. 13; no. 9; p. 5721
Main Authors Zhong, Ming, Ma, Ye, Li, Zhan, He, Jiajian, Liu, Yaxin
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2023
Subjects
Algorithms
Analysis
Automation
Binocular vision
Climbing
Closed loops
Corrosion
Design
Edge detection
Facades
Inspection
Methods
Neural networks
Process parameters
Recognition
Robotics industry
Robots
target recognition
YOLOv5
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Summary:The cleaning and maintenance of large-scale façades is a high-risk industry. Although existing wall-climbing robots can replace humans who work on façade surfaces, it is difficult for them to operate on façade protrusions due to a lack of perception of the surrounding environment. To address this problem, this paper proposes a binocular vision-based method to assist wall-climbing robots in performing autonomous rust removal and painting. The method recognizes façade protrusions through binocular vision, compares the recognition results with an established dimension database to obtain accurate information on the protrusions and then obtains parameters from the process database to guide the operation. Finally, the robot inspects the operation results and dynamically adjusts the process parameters according to the finished results, realizing closed-loop feedback for intelligent operation. The experimental results show that the You Only Look Once version 5 (YOLOv5) recognition algorithm achieves a 99.63% accuracy for façade protrusion recognition and a 93.33% accuracy for the detection of the rust removal effect using the histogram comparison method. The absolute error of the canny edge detection algorithm is less than 3 mm and the average relative error is less than 2%. This paper establishes a vision-based façade operation process with good inspection effect, which provides an effective vision solution for the automation operation of wall-climbing robots on the façade.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13095721