UAV-based bridge geometric shape measurement using automatic bridge component detection and distributed multi-view reconstruction

Abstract

UAV cameras combined with image-based reconstruction are promising non-contact tools for bridge shape inspection while computational efficiency is low, particularly on large-scale image sets. This paper describes an efficient image-based reconstruction pipeline for bridge geometry measurements. A full bridge three-dimensional reconstruction task is decomposed into multiple distributed tasks of reconstructing bridge sub-models using sub-image sets. A deep learning-based object detection method combined with Euclidean clustering is proposed to automatically generate cropped sub-image sets. An image mask generation technique based on 3D-to-2D mesh projection is used to keep valid pixels for camera pose estimation. The proposed method was validated in a laboratory test for arch shape measurement and in a field test for suspension cable shape measurement. The results show significant improvement about computational efficiency without accuracy reduction, compared with conventional image reconstruction methods.