Automatic Pothole Detection by Different Multispectral Band Combinations

The road is one of the main infrastructures that play a significant role in supporting the economy and human social development. Therefore, this study was aimed to automate potholes extraction using UAV multispectral images. The objective of this study was to identify the band combination for pothol...

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Published in:INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 4, INTELLISYS 2023
Main Authors: Zin, Erma Najihah Md; Shaharom, Muhammad Farid Mohd; Khalid, Nafisah; Tahar, Khairul Nizam
Format: Proceedings Paper
Language:English
Published: SPRINGER INTERNATIONAL PUBLISHING AG 2024
Subjects:
Online Access:https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001261694800023
author Zin
Erma Najihah Md; Shaharom
Muhammad Farid Mohd; Khalid
Nafisah; Tahar
Khairul Nizam
spellingShingle Zin
Erma Najihah Md; Shaharom
Muhammad Farid Mohd; Khalid
Nafisah; Tahar
Khairul Nizam
Automatic Pothole Detection by Different Multispectral Band Combinations
Computer Science
author_facet Zin
Erma Najihah Md; Shaharom
Muhammad Farid Mohd; Khalid
Nafisah; Tahar
Khairul Nizam
author_sort Zin
spelling Zin, Erma Najihah Md; Shaharom, Muhammad Farid Mohd; Khalid, Nafisah; Tahar, Khairul Nizam
Automatic Pothole Detection by Different Multispectral Band Combinations
INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 4, INTELLISYS 2023
English
Proceedings Paper
The road is one of the main infrastructures that play a significant role in supporting the economy and human social development. Therefore, this study was aimed to automate potholes extraction using UAV multispectral images. The objective of this study was to identify the band combination for pothole extractions based on multispectral sensors and analyse the 2D pothole model with the actual measurement. The methodology was divided into four stages- planning, data collection, data processing, and data analysis. The DJI Phantom 4 aerial vehicle with a Sequoia+ Multispectral Camera was used to perform the data acquisition. All data acquisition was operated by three different software, namely Pix4Dmapper software, ArcGIS software, and SAGA GIS software. The final outputs generated from this study were orthophoto, combination bands, and the pothole extraction area. Thirteen combination bands, including a single band, were made to identify the best combination band for pothole extraction. There are two types of combination bands- the two-layer combination band and the three-layer combination band. Two types of potholes were examined, namely Pothole A and Pothole B; for Pothole A, out of the 13 combination bands, the best combination band for pothole extraction was the combination of green, red, and red-edge bands, with a zero-difference value between the actual measurement and computed area. Meanwhile, for Pothole B, the best combination band was the NIR, red, and red-edge bands, with a 0.075 m(2) difference value between the actual measurement and computed area.
SPRINGER INTERNATIONAL PUBLISHING AG
2367-3370
2367-3389
2024
825

10.1007/978-3-031-47718-8_23
Computer Science

WOS:001261694800023
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001261694800023
title Automatic Pothole Detection by Different Multispectral Band Combinations
title_short Automatic Pothole Detection by Different Multispectral Band Combinations
title_full Automatic Pothole Detection by Different Multispectral Band Combinations
title_fullStr Automatic Pothole Detection by Different Multispectral Band Combinations
title_full_unstemmed Automatic Pothole Detection by Different Multispectral Band Combinations
title_sort Automatic Pothole Detection by Different Multispectral Band Combinations
container_title INTELLIGENT SYSTEMS AND APPLICATIONS, VOL 4, INTELLISYS 2023
language English
format Proceedings Paper
description The road is one of the main infrastructures that play a significant role in supporting the economy and human social development. Therefore, this study was aimed to automate potholes extraction using UAV multispectral images. The objective of this study was to identify the band combination for pothole extractions based on multispectral sensors and analyse the 2D pothole model with the actual measurement. The methodology was divided into four stages- planning, data collection, data processing, and data analysis. The DJI Phantom 4 aerial vehicle with a Sequoia+ Multispectral Camera was used to perform the data acquisition. All data acquisition was operated by three different software, namely Pix4Dmapper software, ArcGIS software, and SAGA GIS software. The final outputs generated from this study were orthophoto, combination bands, and the pothole extraction area. Thirteen combination bands, including a single band, were made to identify the best combination band for pothole extraction. There are two types of combination bands- the two-layer combination band and the three-layer combination band. Two types of potholes were examined, namely Pothole A and Pothole B; for Pothole A, out of the 13 combination bands, the best combination band for pothole extraction was the combination of green, red, and red-edge bands, with a zero-difference value between the actual measurement and computed area. Meanwhile, for Pothole B, the best combination band was the NIR, red, and red-edge bands, with a 0.075 m(2) difference value between the actual measurement and computed area.
publisher SPRINGER INTERNATIONAL PUBLISHING AG
issn 2367-3370
2367-3389
publishDate 2024
container_volume 825
container_issue
doi_str_mv 10.1007/978-3-031-47718-8_23
topic Computer Science
topic_facet Computer Science
accesstype
id WOS:001261694800023
url https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001261694800023
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