UAV Imagery-based Automatic Classification of Ground Surface Types for Earthworks

The construction industry is introducing autonomous heavy equipment to overcome labor shortages and improve productivity. For autonomous heavy equipment to work on earthmoving at sites, the equipment needs to recognize and understand ground surface types. However, the ground surface types are manual...

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Bibliographic Details
Published in:KSCE journal of civil engineering 2024, 28(6), , pp.2121-2131
Main Authors: Won, Daeyoun, Chi, Seokho, Choi, Jin Ouk
Format: Article
Language:English
Subjects:
Artificial neural networks
Civil Engineering
Classification
Construction
Construction industry
Construction Management
Construction sites
Deep learning
Earthmoving
Earthmoving equipment
Engineering
Geotechnical Engineering & Applied Earth Sciences
Image acquisition
Image classification
Industrial Pollution Prevention
Labels
Machine learning
Neural networks
Unmanned aerial vehicles
토목공학
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Summary:The construction industry is introducing autonomous heavy equipment to overcome labor shortages and improve productivity. For autonomous heavy equipment to work on earthmoving at sites, the equipment needs to recognize and understand ground surface types. However, the ground surface types are manually inspected in practice, and related studies are lacking. To address this issue, the authors developed and tested models that automatically classify ground surface types from images acquired by an unmanned aerial vehicle using a deep learning-based multi-label classification method that applies Binary Relevance (BR) and Label Powerset (LP) methods with Residual Neural Network (ResNet) and Vision Transformer classification network (VIT). The model performances were comparatively evaluated through experiments conducted on actual construction sites. The results showed that the BR model with ResNet is the best model in terms of automated ground surface type identification during earthmoving. The results are expected to broaden the understanding of complex and expansive construction sites for autonomous vehicles and thus facilitate deployment of autonomous heavy equipment by helping them to understand working areas and any obstacles on construction sites quickly and effectively, which will reduce the cost and time needed for on-site ground surface management.
ISSN:1226-7988
1976-3808
DOI:10.1007/s12205-024-1643-x