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 façade. These robots are also required to apply diff...
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doaj-4d06e6c9e09448ae8e0b709ea9c124852021-03-30T03:32:48ZengIEEEIEEE Access2169-35362020-01-01818001018002110.1109/ACCESS.2020.30278399210101Contaminated Facade Identification Using Convolutional Neural Network and Image ProcessingJiseok Lee0https://orcid.org/0000-0001-6182-9662Jooyoung Hong1https://orcid.org/0000-0003-0629-7565Garam Park2https://orcid.org/0000-0003-0930-9699Hwa Soo Kim3https://orcid.org/0000-0001-7116-7367Sungon Lee4https://orcid.org/0000-0003-1313-3403Taewon Seo5https://orcid.org/0000-0001-9447-7675School of Mechanical Engineering, Hanyang University, Seoul, South KoreaSchool of Mechanical and Aerospace Engineering, Seoul National University, Seoul, South KoreaSchool of Mechanical Engineering, Hanyang University, Seoul, South KoreaSchool of Mechanical System Engineering, Kyounggi University, Suwon, South KoreaSchool of Electrical Engineering, Hanyang University, Ansan, South KoreaSchool of Mechanical Engineering, Hanyang University, Seoul, South KoreaIn 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 façade. 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 façade. 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.https://ieeexplore.ieee.org/document/9210101/Contaminant detectionconvolutional neural networkfaçade cleaningimage processing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jiseok Lee Jooyoung Hong Garam Park Hwa Soo Kim Sungon Lee Taewon Seo |
spellingShingle |
Jiseok Lee Jooyoung Hong Garam Park Hwa Soo Kim Sungon Lee Taewon Seo Contaminated Facade Identification Using Convolutional Neural Network and Image Processing IEEE Access Contaminant detection convolutional neural network façade cleaning image processing |
author_facet |
Jiseok Lee Jooyoung Hong Garam Park Hwa Soo Kim Sungon Lee Taewon Seo |
author_sort |
Jiseok Lee |
title |
Contaminated Facade Identification Using Convolutional Neural Network and Image Processing |
title_short |
Contaminated Facade Identification Using Convolutional Neural Network and Image Processing |
title_full |
Contaminated Facade Identification Using Convolutional Neural Network and Image Processing |
title_fullStr |
Contaminated Facade Identification Using Convolutional Neural Network and Image Processing |
title_full_unstemmed |
Contaminated Facade Identification Using Convolutional Neural Network and Image Processing |
title_sort |
contaminated facade identification using convolutional neural network and image processing |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
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 façade. 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 façade. 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. |
topic |
Contaminant detection convolutional neural network façade cleaning image processing |
url |
https://ieeexplore.ieee.org/document/9210101/ |
work_keys_str_mv |
AT jiseoklee contaminatedfacadeidentificationusingconvolutionalneuralnetworkandimageprocessing AT jooyounghong contaminatedfacadeidentificationusingconvolutionalneuralnetworkandimageprocessing AT garampark contaminatedfacadeidentificationusingconvolutionalneuralnetworkandimageprocessing AT hwasookim contaminatedfacadeidentificationusingconvolutionalneuralnetworkandimageprocessing AT sungonlee contaminatedfacadeidentificationusingconvolutionalneuralnetworkandimageprocessing AT taewonseo contaminatedfacadeidentificationusingconvolutionalneuralnetworkandimageprocessing |
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