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...

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Bibliographic Details
Published in:	International journal of remote sensing Vol. 45; no. 13; pp. 4449 - 4473
Main Authors:	Khataan, Ahmed, Gargoum, Suliman
Format:	Journal Article
Language:	English
Published:	London Taylor & Francis 02.07.2024 Taylor & Francis Ltd
Subjects:	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
Online Access:	Get full text
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