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In China, coal-fired power plants, which mainly rely on coal, require timely inventory of coal storage to calculate economic benefits. How to achieve accurate measurement of coal storage has always been a difficult problem that coal-fired power plants urgently need to solve. For precise measurement of coal storage capacity of unmanned aerial vehicles (UAVs) in closed coal yards, the three-dimensional model through the three-dimensional (3D) reconstruction of oblique images is a highly valuable research path for engineering applications. This paper studies on the 3D reconstruction technology of oblique images based on UAVs vision. In terms of image enhancement, an enhancement function with offset is introduced to highlight image edges while preserving background information. For image feature extraction and matching, speeded up robust feature (SURF) image recognition and square filtering integral graph acceleration are adopted to meet real-time and robustness requirements. When extending dense point clouds with rich details in sparse point clouds, the area adaptive algorithm is used to meet the requirements of regional smoothing and surface changes. In the Poisson algorithm for 3D mesh reconstruction, Gaussian filtering is used instead of 3rd-order mean filtering to maintain the surface topology of the point cloud model. A 3D reconstruction of a closed coal yard in a coal-fired power plant was carried out using UAVs, and the results showed that the accuracy of the algorithm was improved by 4.5%, and the integrity by 2.22%. This technical solution meets the requirements of UAVs oblique image 3D reconstruction in closed coal yards, achieving accurate coal quantity statistics, timely inventory and transportation, stable boiler combustion, and successfully improving the accuracy and completeness of visual based 3D reconstruction.
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