Han, S; Jiang, Y and Bai, Y (2022) Fast-pgmed: Fast and dense elevation determination for earthwork using drone and deep learning. Journal of Construction Engineering and Management, 148(4): 04022008, ISSN 0733-9364
Abstract
This paper presents a time- A nd cost-effective elevation determination method for earthwork operations using ready-to-fly imaging drones and deep learning technologies. The proposed method is named the fast pixel grid/group matching and elevation determination (Fast-PGMED) algorithm. The input data are a pair of approximate 2:1-scale top-view images, and the output is the determined elevation map for the scanned station. Feature matching of the two multiscale images is conducted by calculating correlations between target patch predictions (via DeepMatchNet, a fully convolutional network) and potential target patches (via virtual elevation model). The overall processing time is about 21 s (including 5 s for low-high orthoimage assembly, 3 s for patch feature generation, and 13 s for pixel matching) to process a 2,500-pixel grid, and the generated elevation values are as accurate as photogrammetry (within 5-cm error) but took much less time. Moreover, the developed method has been evaluated with two different drones. Volume measurement was quickly conducted via 2D elevation maps and accurately estimated via dense point clouds and Civil 3D.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | elevation algorithm; feature matching; fully convolutional network; imaging drones; multiprocessing |
| Index terms: | earthwork, point cloud, maps, drone, deep learning |
| Subjects: | artificial intelligence, construction methods, spatial and geospatial analysis, digital design, automation and robotics |
| Topics: | Digital Applications, Research Practice, Engineering Principles |
| Descriptive scope: | 2 PC |
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