Contaminated facade identification using convolutional neural network and image processing

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.