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Detection of Highway Pavement Damage Based on a CNN Using Grayscale and HOG Features

Aiming at the demand for rapid detection of highway pavement damage, many deep learning methods based on convolutional neural networks (CNNs) have been developed. However, CNN methods with raw image data require a high-performance hardware configuration and cost machine time. To reduce machine time...

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Detalles Bibliográficos
Autores principales: Chen, Guo-Hong, Ni, Jie, Chen, Zhuo, Huang, Hao, Sun, Yun-Lei, Ip, Wai Hung, Yung, Kai Leung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002920/
https://www.ncbi.nlm.nih.gov/pubmed/35408070
http://dx.doi.org/10.3390/s22072455
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author Chen, Guo-Hong
Ni, Jie
Chen, Zhuo
Huang, Hao
Sun, Yun-Lei
Ip, Wai Hung
Yung, Kai Leung
author_facet Chen, Guo-Hong
Ni, Jie
Chen, Zhuo
Huang, Hao
Sun, Yun-Lei
Ip, Wai Hung
Yung, Kai Leung
author_sort Chen, Guo-Hong
collection PubMed
description Aiming at the demand for rapid detection of highway pavement damage, many deep learning methods based on convolutional neural networks (CNNs) have been developed. However, CNN methods with raw image data require a high-performance hardware configuration and cost machine time. To reduce machine time and to apply the detection methods in common scenarios, the CNN structure with preprocessed image data needs to be simplified. In this work, a detection method based on a CNN and the combination of the grayscale and histogram of oriented gradients (HOG) features is proposed. First, the Gamma correction was employed to highlight the grayscale distribution of the damage area, which compresses the space of normal pavement. The preprocessed image was then divided into several unit cells, whose grayscale and HOG were calculated, respectively. The grayscale and HOG of each unit cell were combined to construct the grayscale-weighted HOG (GHOG) feature patterns. These feature patterns were input to the CNN with a specific structure and parameters. The trained indices suggested that the performance of the GHOG-based method was significantly improved, compared with the traditional HOG-based method. Furthermore, the GHOG-feature-based CNN technique exhibited flexibility and effectiveness under the same accuracy, in comparison to those deep learning techniques that directly deal with raw data. Since the grayscale has a definite physical meaning, the present detection method possesses a potential application for the further detection of damage details in the future.
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spelling pubmed-90029202022-04-13 Detection of Highway Pavement Damage Based on a CNN Using Grayscale and HOG Features Chen, Guo-Hong Ni, Jie Chen, Zhuo Huang, Hao Sun, Yun-Lei Ip, Wai Hung Yung, Kai Leung Sensors (Basel) Communication Aiming at the demand for rapid detection of highway pavement damage, many deep learning methods based on convolutional neural networks (CNNs) have been developed. However, CNN methods with raw image data require a high-performance hardware configuration and cost machine time. To reduce machine time and to apply the detection methods in common scenarios, the CNN structure with preprocessed image data needs to be simplified. In this work, a detection method based on a CNN and the combination of the grayscale and histogram of oriented gradients (HOG) features is proposed. First, the Gamma correction was employed to highlight the grayscale distribution of the damage area, which compresses the space of normal pavement. The preprocessed image was then divided into several unit cells, whose grayscale and HOG were calculated, respectively. The grayscale and HOG of each unit cell were combined to construct the grayscale-weighted HOG (GHOG) feature patterns. These feature patterns were input to the CNN with a specific structure and parameters. The trained indices suggested that the performance of the GHOG-based method was significantly improved, compared with the traditional HOG-based method. Furthermore, the GHOG-feature-based CNN technique exhibited flexibility and effectiveness under the same accuracy, in comparison to those deep learning techniques that directly deal with raw data. Since the grayscale has a definite physical meaning, the present detection method possesses a potential application for the further detection of damage details in the future. MDPI 2022-03-23 /pmc/articles/PMC9002920/ /pubmed/35408070 http://dx.doi.org/10.3390/s22072455 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Chen, Guo-Hong
Ni, Jie
Chen, Zhuo
Huang, Hao
Sun, Yun-Lei
Ip, Wai Hung
Yung, Kai Leung
Detection of Highway Pavement Damage Based on a CNN Using Grayscale and HOG Features
title Detection of Highway Pavement Damage Based on a CNN Using Grayscale and HOG Features
title_full Detection of Highway Pavement Damage Based on a CNN Using Grayscale and HOG Features
title_fullStr Detection of Highway Pavement Damage Based on a CNN Using Grayscale and HOG Features
title_full_unstemmed Detection of Highway Pavement Damage Based on a CNN Using Grayscale and HOG Features
title_short Detection of Highway Pavement Damage Based on a CNN Using Grayscale and HOG Features
title_sort detection of highway pavement damage based on a cnn using grayscale and hog features
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9002920/
https://www.ncbi.nlm.nih.gov/pubmed/35408070
http://dx.doi.org/10.3390/s22072455
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