Abstract
In recent years, as number of new building getting larger, there has been an increased interest in the cleaning of exterior walls. Accordingly, there is a growing interest in automatic cleaning robots that move around the outer building facade. These robots are also required to apply different cleaning methods to remove various contaminants on the outer wall of the building. However, current surface contaminant detection systems can either detect only a single type of contaminant, or are not compact enough for installation on mobile platforms that move around the outer facade. As cleaning workers are able to distinguish various contaminants with the naked eye, we aim to solve this problem by developing a machine-vision system using convolutional neural networks (CNNs) and image processing methods. As it is a compact system that uses only a camera to take pictures and a processor to process the images, it is suitable for applications involving mobile platforms. Object-type contaminants such as avian feces are handled by the YOLOv3 module using the object-detection algorithm. Area-type contaminants such as rusty stains are processed using the color-detection module using the HSV color space, median filter, and flood fill algorithm. Particle-type contaminants such as dust are handled by the grayscale module, converting images to grayscale images and then comparing the average brightness with a reference that is provided in advance. This proposed machine vision system will detect objects, areas, and particle-type contaminants with a single image and some reference images provided in advance.
Highlights
In recent years, with the increase in the number of cases involving environmental pollutants such as sand dust or fine dust, the outer walls of buildings have tended to accumulate more dirt [1]
Many researchers have developed outer wall cleaning robots that replace the humans who perform this task manually [2]–[7]. Equipping these cleaning robots with systems that detect contaminants on outer walls will enable them to perform a greater variety of tasks
If the number and type of various contaminants in the cleaning area are detected in real time, it is possible to automatically spray the cleaning liquid that corresponds to the pollutant, and more efficient cleaning can be performed by varying the force of the brush
Summary
With the increase in the number of cases involving environmental pollutants such as sand dust or fine dust, the outer walls of buildings have tended to accumulate more dirt [1]. The purpose of this study is detecting the various types of contaminants in building façade such as avian feces, rusty stains, and dusts in a straight process in framework. Inspiring from that workers cleaning the outer walls of buildings detect these contaminants only by ‘naked eye’, we used machine vision to solve the problem. We propose simple framework using various modules of common conventional image processing methods, setting a straight processing framework to detect various contaminants at once, modifying each method as little as possible.
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