Abstract
As the number of high-rise buildings is increasing, more methods of exterior-wall cleaning are being developed. There are a few models based on artificial intelligence that determine the type and level of contamination primarily by moving the cleaning area. In this study, we propose an system using YOLOv3 algorithm, color-detection, to install on facade cleaning robot and brightness-discrimination. There are three types of contaminant-detection parameters: size, color, and brightness, and these parameters are subjected to a robust optimization process to maintain a constant detection rate under different conditions. The three parameters are determined via Taguchi method with signal to noise ratio and noise factors. An environment for algorithm testing is established, and artificial contamination is implemented on the specimen. A field test with the detection algorithm shall be performed in the near future.
Highlights
In recent years, as the number of high-rise buildings has increased, the market demand for exterior-wall cleaning service for such buildings has increased
Because workers in the field identify contaminants with the naked eye, we propose a machine-vision system that uses CNN(convolutional neural network) and image processing [9]
We use the Taguchi method to tune the system parameters such that the machine-vision system described earlier is robust to various external brightness levels and distance from the facade [12], [13]
Summary
As the number of high-rise buildings has increased, the market demand for exterior-wall cleaning service for such buildings has increased. Based on the type of contaminant, an appropriate cleaning solution can be automatically identified, and the contamination of the exterior wall can be transmitted to a central management system that shall automatically monitor the cleanliness of the wall To develop this exterior-wall contaminant-detection system, we searched for a surface-contaminant-detection method. There are two methods of detecting contamination of exterior-wall surface. Liquid contaminants are not the only type of contaminants on the exterior walls of buildings This can give rise to an error in the detection result, thereby making this method difficult to apply. The contaminants on the exterior walls of buildings are classified into three types: object, area, and particle. We use the Taguchi method to tune the system parameters such that the machine-vision system described earlier is robust to various external brightness levels and distance from the facade [12], [13].
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