Cargando…

Smartphone Sensing of Road Surface Condition and Defect Detection

Road surface condition is vitally important for road safety and transportation efficiency. Conventionally, road surface monitoring relies on specialised vehicles equipped with professional devices, but such dedicated large-scale road surveying is usually costly, time-consuming, and prohibitively dif...

Descripción completa

Detalles Bibliográficos
Autores principales: Dong, Dapeng, Li, Zili
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8401562/
https://www.ncbi.nlm.nih.gov/pubmed/34450875
http://dx.doi.org/10.3390/s21165433
_version_ 1783745580344803328
author Dong, Dapeng
Li, Zili
author_facet Dong, Dapeng
Li, Zili
author_sort Dong, Dapeng
collection PubMed
description Road surface condition is vitally important for road safety and transportation efficiency. Conventionally, road surface monitoring relies on specialised vehicles equipped with professional devices, but such dedicated large-scale road surveying is usually costly, time-consuming, and prohibitively difficult for frequent pavement condition monitoring—for example, on an hourly or daily basis. Current advances in technologies such as smartphones, machine learning, big data, and cloud analytics have enabled the collection and analysis of a great amount of field data from numerous users (e.g., drivers) whilst driving on roads. In this regard, we envisage that a smartphone equipped with an accelerometer and GPS sensors could be used to collect road surface condition information much more frequently than specialised equipment. In this study, accelerometer data were collected at low rate from a smartphone via an Android-based application over multiple test-runs on a local road in Ireland. These data were successfully processed using power spectral density analysis, and defects were later identified using a k-means unsupervised machine learning algorithm, resulting in an average accuracy of 84%. Results demonstrated the potential of collecting crowdsourced data from a large population of road users for road surface defect detection on a quasi-real-time basis. This frequent reporting on a daily/hourly basis can be used to inform the relevant stakeholders for timely road maintenance, aiming to ensure the road’s serviceability at a lower inspection and maintenance cost.
format Online
Article
Text
id pubmed-8401562
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-84015622021-08-29 Smartphone Sensing of Road Surface Condition and Defect Detection Dong, Dapeng Li, Zili Sensors (Basel) Article Road surface condition is vitally important for road safety and transportation efficiency. Conventionally, road surface monitoring relies on specialised vehicles equipped with professional devices, but such dedicated large-scale road surveying is usually costly, time-consuming, and prohibitively difficult for frequent pavement condition monitoring—for example, on an hourly or daily basis. Current advances in technologies such as smartphones, machine learning, big data, and cloud analytics have enabled the collection and analysis of a great amount of field data from numerous users (e.g., drivers) whilst driving on roads. In this regard, we envisage that a smartphone equipped with an accelerometer and GPS sensors could be used to collect road surface condition information much more frequently than specialised equipment. In this study, accelerometer data were collected at low rate from a smartphone via an Android-based application over multiple test-runs on a local road in Ireland. These data were successfully processed using power spectral density analysis, and defects were later identified using a k-means unsupervised machine learning algorithm, resulting in an average accuracy of 84%. Results demonstrated the potential of collecting crowdsourced data from a large population of road users for road surface defect detection on a quasi-real-time basis. This frequent reporting on a daily/hourly basis can be used to inform the relevant stakeholders for timely road maintenance, aiming to ensure the road’s serviceability at a lower inspection and maintenance cost. MDPI 2021-08-12 /pmc/articles/PMC8401562/ /pubmed/34450875 http://dx.doi.org/10.3390/s21165433 Text en © 2021 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 Article
Dong, Dapeng
Li, Zili
Smartphone Sensing of Road Surface Condition and Defect Detection
title Smartphone Sensing of Road Surface Condition and Defect Detection
title_full Smartphone Sensing of Road Surface Condition and Defect Detection
title_fullStr Smartphone Sensing of Road Surface Condition and Defect Detection
title_full_unstemmed Smartphone Sensing of Road Surface Condition and Defect Detection
title_short Smartphone Sensing of Road Surface Condition and Defect Detection
title_sort smartphone sensing of road surface condition and defect detection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8401562/
https://www.ncbi.nlm.nih.gov/pubmed/34450875
http://dx.doi.org/10.3390/s21165433
work_keys_str_mv AT dongdapeng smartphonesensingofroadsurfaceconditionanddefectdetection
AT lizili smartphonesensingofroadsurfaceconditionanddefectdetection