Real-Time Pavement Crack Detection Based on Artificial Intelligence

Pavement as a structural element is closely related to the road traffic system because it is essentially a support structure for the movement of vehicles. Modern technology has attained significant improvements in road durability, quality, and safety through the use of new materials and construction...

Full description

Bibliographic Details
Published in:Journal of Advanced Research in Applied Sciences and Engineering Technology
Main Author: Ya’acob N.; Zuraimi M.D.I.; Rahman A.A.A.; Yusof A.L.; Ali D.M.
Format: Article
Language:English
Published: Semarak Ilmu Publishing 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184455240&doi=10.37934%2faraset.38.2.7182&partnerID=40&md5=6128ac2760fcd8a8e5cb1dd95c7ad7b5
id 2-s2.0-85184455240
spelling 2-s2.0-85184455240
Ya’acob N.; Zuraimi M.D.I.; Rahman A.A.A.; Yusof A.L.; Ali D.M.
Real-Time Pavement Crack Detection Based on Artificial Intelligence
2024
Journal of Advanced Research in Applied Sciences and Engineering Technology
38
2
10.37934/araset.38.2.7182
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184455240&doi=10.37934%2faraset.38.2.7182&partnerID=40&md5=6128ac2760fcd8a8e5cb1dd95c7ad7b5
Pavement as a structural element is closely related to the road traffic system because it is essentially a support structure for the movement of vehicles. Modern technology has attained significant improvements in road durability, quality, and safety through the use of new materials and construction technology. All civil engineering infrastructures have a specific lifespan. The road surface can be the worst, and repair is more costly if the pavement is not maintained. The objective of this research is to spot the damages over the inspected road and detect cracks in a short time, useful to achieve more comprehensive monitoring and assessment of the condition of road pavement using a smartphone equipped with You Only Look Once (YOLO) to reduce maintenance costs. The method uses advanced image processing techniques and uses YOLO to detect cracked pavement in a short time. More specifically, it is based on the latest generation of Deep Neural Network (DNN) algorithms, such as YOLO V5. YOLO V5 is used as a detector for cracked pavement and image processing as an automated pavement distress detector. The obtained result for longitudinal cracks shows that the area of cracks is 1.1682 m2, with a threshold value of 0.91, while the result for transversal cracks shows the area of cracks of 1.9627 m2, with a threshold value of 0.85. Otsu’s method works best under conditions such as low noise level, homogeneous lighting, and higher intra-class variance than an inter-class variance. The outcome of this project is improved recognition accuracy compared to the manual recognition currently used. © 2024, Semarak Ilmu Publishing. All rights reserved.
Semarak Ilmu Publishing
24621943
English
Article
All Open Access; Hybrid Gold Open Access
author Ya’acob N.; Zuraimi M.D.I.; Rahman A.A.A.; Yusof A.L.; Ali D.M.
spellingShingle Ya’acob N.; Zuraimi M.D.I.; Rahman A.A.A.; Yusof A.L.; Ali D.M.
Real-Time Pavement Crack Detection Based on Artificial Intelligence
author_facet Ya’acob N.; Zuraimi M.D.I.; Rahman A.A.A.; Yusof A.L.; Ali D.M.
author_sort Ya’acob N.; Zuraimi M.D.I.; Rahman A.A.A.; Yusof A.L.; Ali D.M.
title Real-Time Pavement Crack Detection Based on Artificial Intelligence
title_short Real-Time Pavement Crack Detection Based on Artificial Intelligence
title_full Real-Time Pavement Crack Detection Based on Artificial Intelligence
title_fullStr Real-Time Pavement Crack Detection Based on Artificial Intelligence
title_full_unstemmed Real-Time Pavement Crack Detection Based on Artificial Intelligence
title_sort Real-Time Pavement Crack Detection Based on Artificial Intelligence
publishDate 2024
container_title Journal of Advanced Research in Applied Sciences and Engineering Technology
container_volume 38
container_issue 2
doi_str_mv 10.37934/araset.38.2.7182
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184455240&doi=10.37934%2faraset.38.2.7182&partnerID=40&md5=6128ac2760fcd8a8e5cb1dd95c7ad7b5
description Pavement as a structural element is closely related to the road traffic system because it is essentially a support structure for the movement of vehicles. Modern technology has attained significant improvements in road durability, quality, and safety through the use of new materials and construction technology. All civil engineering infrastructures have a specific lifespan. The road surface can be the worst, and repair is more costly if the pavement is not maintained. The objective of this research is to spot the damages over the inspected road and detect cracks in a short time, useful to achieve more comprehensive monitoring and assessment of the condition of road pavement using a smartphone equipped with You Only Look Once (YOLO) to reduce maintenance costs. The method uses advanced image processing techniques and uses YOLO to detect cracked pavement in a short time. More specifically, it is based on the latest generation of Deep Neural Network (DNN) algorithms, such as YOLO V5. YOLO V5 is used as a detector for cracked pavement and image processing as an automated pavement distress detector. The obtained result for longitudinal cracks shows that the area of cracks is 1.1682 m2, with a threshold value of 0.91, while the result for transversal cracks shows the area of cracks of 1.9627 m2, with a threshold value of 0.85. Otsu’s method works best under conditions such as low noise level, homogeneous lighting, and higher intra-class variance than an inter-class variance. The outcome of this project is improved recognition accuracy compared to the manual recognition currently used. © 2024, Semarak Ilmu Publishing. All rights reserved.
publisher Semarak Ilmu Publishing
issn 24621943
language English
format Article
accesstype All Open Access; Hybrid Gold Open Access
record_format scopus
collection Scopus
_version_ 1809677674191257600