Abstract
Fast reconstruction of power lines and corridors is a critical task in UAV (unmanned aerial vehicle)-based inspection of high-voltage transmission corridors. However, recent dense matching algorithms suffer the problem of low efficiency when processing large-scale high-resolution UAV images. This study proposes an efficient dense matching method for the 3D reconstruction of high-voltage transmission corridors with fine-scale power lines. First, an efficient random red-black checkerboard propagation is proposed, which utilizes the neighbor pixels with the most similar color to propagate plane parameters. To combine the pixel-wise view selection strategy adopted in Colmap with the efficient random red-black checkerboard propagation, the updating schedule for inferring visible probability is improved; second, strategies for decreasing the number of matching cost computations are proposed, which can reduce the unnecessary hypotheses for verification. The number of neighbor pixels necessary to propagate plane parameters is reduced with the increase of iterations, and the number of the combinations of depth and normal is reduced for the pixel with better matching cost in the plane refinement step; third, an efficient GPU (graphics processing unit)-based depth map fusion method is proposed, which employs a weight function based on the reprojection errors to fuse the depth map. Finally, experiments are conducted by using three UAV datasets, and the results indicate that the proposed method can maintain the completeness of power line reconstruction with high efficiency when compared to other PatchMatch-based methods. In addition, two benchmark datasets are used to verify that the proposed method can achieve a better F1 score, 4–7 times faster than Colmap.
Highlights
In high-voltage transmission corridor scenarios, the power line is one of the key elements that should be regularly inspected by power production and maintenance departments
Since the rectangle closed-loop trajectory is adopted in test site 1, the maximum number of views selected for PatchMatch is set to 10 to ensure that the side-overlapping images can be selected to reconstruct more stable power lines, while it is set to 5 in test site 2 and test site 3
In the depth-map fusion, the normal angle constraint is not taken into consideration with all the methods since the normal of power lines estimated by PatchMatch is not accurate
Summary
In high-voltage transmission corridor scenarios, the power line is one of the key elements that should be regularly inspected by power production and maintenance departments. UAV photogrammetric systems equipped with optical cameras have been extensively used for data acquisition of transmission corridors, and a large number of high-resolution UAV images can be collected rapidly to achieve offsite visual inspection of power lines by using 3D point clouds of transmission corridors [1]. According to the work of [14], MVS methods can be divided into four groups: surfaceevolution-based methods [15,16], voxel-based methods [17], patch-based methods [18,19], and depth-map-based methods [20,21,22] As it is suitable for 3D dense matching of largescale scenes, depth-map-based methods have been widely used, which can be verified from traditional dense matching algorithms, e.g., SGM (semi-global matching) [23] and PMVS (patch-based multi-view stereo) [18], to the recent PatchMatch-based methods.
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