Fast-PGMED: Fast and Dense Elevation Determination for Earthwork Using Drone and Deep Learning
نویسندگان
چکیده
This paper presents a time- and cost-effective elevation determination method for earthwork operations using ready-to-fly imaging drones deep learning technologies. The proposed is named the fast pixel grid/group matching (Fast-PGMED) algorithm. input data are pair of approximate 2:1-scale top-view images, output determined map scanned station. Feature two multiscale images conducted by calculating correlations between target patch predictions (via DeepMatchNet, fully convolutional network) potential patches virtual model). overall processing time about 21 s (including 5 low-high orthoimage assembly, 3 feature generation, 13 matching) to process 2,500-pixel grid, generated values as accurate photogrammetry (within 5-cm error) but took much less time. Moreover, developed has been evaluated with different drones. Volume measurement was quickly via 2D maps accurately estimated dense point clouds Civil 3D.
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ژورنال
عنوان ژورنال: Journal of the Construction Division and Management
سال: 2022
ISSN: ['1943-7862', '0733-9364']
DOI: https://doi.org/10.1061/(asce)co.1943-7862.0002256