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- Title
Mobile LiDAR-based Real-time Identification of Transmission Lines.
- Authors
Li, Minglei; Xu, Li; Li, Mingfan; Qu, Guoyuan; Wei, Dazhou; Li, Wei
- Abstract
This paper proposes a method for identifying 3D point cloud of transmission line acquired by light detection and ranging (LiDAR) real-time mobile scanning. Since the single frame of point cloud obtained by LiDAR is sparse, the method employs a sliding spatial window strategy with Kalman filtering for dynamic point cloud registration. Then, a 3D point cloud deep learning neural network that utilizes uniform sampling and local feature aggregation (LFA) is designed specifically for transmission line objects. The network handles the problem of long-span objects and a large amount of point cloud. Finally, the instantiated transmission line objects are extracted from the top-down projection of the semantically segmented 3D point cloud by fast Euclidean clustering algorithm. Experiments demonstrate that the method achieves a classification accuracy of 94.7% and a mean intersection over union of 81.6% on 3D point cloud datasets of transmission line obtained from LiDAR mobile scanning, validating its ability to achieve real-time identification and distance measurement of transmission line objects.
- Subjects
ELECTRIC lines; DOPPLER lidar; OPTICAL radar; LIDAR; POINT cloud; EUCLIDEAN algorithm; IDENTIFICATION
- Publication
International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 2024, Vol 48, Issue 1, p335
- ISSN
1682-1750
- Publication type
Article
- DOI
10.5194/isprs-archives-XLVIII-1-2024-335-2024