Analysis of accuracy factor and pre-processing methodology of image compensation for 3D reconstruction using 2D image obtained from unmanned aerial vehicle (UAV)

Abstract

Unmanned aerial vehicles (UAVs) are widely utilized in civil engineering due to the ease of use of UAVs in aerial monitoring tasks. A UAV-based mapping system is efficient and allows for a higher frequency of monitoring. Furthermore, improving technologies with increasing applications has made it possible to wirelessly send auto flight data to ground stations in real time. Existing studies have focused on data processing on UAV captured data to a form that is measurable and manageable. Studies on using 2D images for 3D modeling usually employ the point cloud generation algorithm. This research explores a 2D image environment that is favorable for point cloud generation by adjusting the environmental values of the image that have a direct influence on the algorithm of point cloud generation. Accuracy analysis is done by checking the number of generated point clouds by variation of illumination values. The aim of this study is to propose a pre-processing methodology to adjust 2D image using histogram equalization, adaptive histogram equalization, image sharpening and local contrast enhancement method in construction site. 2D image corrections by pre-processing methods are converted into point cloud data and results performance is analyzed and verified by comparing with original data results.

Keywords

• uav
• photogrammetry
• as-built model
• image compensation
• point cloud