ArticleSensors (Basel, Switzerland)2020
A Rapid Method of the Rock Mass Surface Reconstruction for Surface Deformation Detection at Close Range.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 6 citations in OpenAlex.
- Unmanned aerial vehicle based multi-person detection via deep neural network models.Frontiers in neurorobotics · 2025Article
- Experimental study on the punching failure model of soft red rock subgrade by BPT-BVM methods and it's assessment of the load bearing capacity.Scientific reports · 2024Article
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7 authors at 2 institutions in 1 country.
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Abstract
Characterizing the surface deformation during the inter-survey period could assist in understanding rock mass progressive failure processes. Moreover, 3D reconstruction of rock mass surface is a crucial step in surface deformation detection. This study presents a method to reconstruct the rock mass surface at close range in a fast way using the improved structure from motion-multi view stereo (SfM) algorithm for surface deformation detection. To adapt the unique feature of rock mass surface, the AKAZE algorithm with the best performance in rock mass feature detection is introduced to improve SfM. The surface reconstructing procedure mainly consists of image acquisition, feature point detection, sparse reconstruction, and dense reconstruction. Hereafter, the proposed method was verified by three experiments. Experiment 1 showed that this method effectively reconstructed the rock mass model. Experiment 2 proved the advanced accuracy of the improved SfM compared with the traditional one in reconstructing the rock mass surface. Eventually, in Experiment 3, the surface deformation of rock mass was quantified through reconstructing images before and after the disturbance. All results have shown that the proposed method could provide reliable information in rock mass surface reconstruction and deformation detection.
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