Automated traffic sign change detection using low-cost LiDAR scans and unsupervised machine learning

Current practices in traffic sign monitoring heavily rely on manual inspections, a method that is both time-consuming and prone to human error. This leads to inefficiencies in the management and maintenance of these critical roadside assets. The objective of this work is to overcome these limitation...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:International journal of remote sensing Ročník 45; číslo 13; s. 4449 - 4473
Hlavní autoři: Khataan, Ahmed, Gargoum, Suliman
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Taylor & Francis 02.07.2024
Taylor & Francis Ltd
Témata:
Algorithms
Asset management
assets
automated detection
Automation
Change detection
cost effectiveness
data collection
Highway maintenance
Human error
Inspection
KD-tree algorithms
Lidar
LiDAR point cloud
Machine learning
Monitoring
Roads & highways
Roadsides
Signs
Traffic
Traffic control
traffic sign change detection
Traffic signs
Unsupervised learning
voxelization
ISSN:0143-1161, 1366-5901, 1366-5901
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Current practices in traffic sign monitoring heavily rely on manual inspections, a method that is both time-consuming and prone to human error. This leads to inefficiencies in the management and maintenance of these critical roadside assets. The objective of this work is to overcome these limitations by proposing a method for automated change detection in traffic signs using low-density LiDAR data. The proposed solution integrates noise elimination, point cloud restructuring, and cross-scan KD-tree generation, followed by the application of unsupervised machine learning techniques for change identification. The effectiveness of this method was verified by testing across three different highways with varying point cloud resolutions. For robust testing, an algorithm was also designed to simulate a broad range of different damage scenarios in traffic signs of different types, sizes, and placements. Testing in different scenarios along almost 15 km of the road revealed impressive results with accuracy and F1 score metrics ranging from 92% to 100%. Moreover, the algorithm was also extremely efficient with an average runtime of just 115" per km of fully automated unattended processing. The change detection potential of the proposed algorithm extends beyond traffic signs, as it could be adapted for many highway elements, enhancing the efficiency of transportation asset management and highway maintenance programmes. The findings indicate that this approach not only fills a significant gap in the current traffic sign monitoring and asset management practice but also offers a promising, comprehensive solution towards automated, cost-effective, and precise monitoring and maintenance of traffic signs, thus addressing a major challenge in this area.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0143-1161
1366-5901
1366-5901
DOI:10.1080/01431161.2024.2365813