High‐resolution model reconstruction and bridge damage detection based on data fusion of unmanned aerial vehicles light detection and ranging data imagery

Abstract

AbstractDamage detection is essential for the maintenance of transportation infrastructure that experiences high daily traffic levels in potentially extreme environments and changes in use patterns. However, traditional physical inspection is always labor‐intensive, subjective, and biased, lacking the objective perspective required for a comprehensive and reliable assessment. Recently, unmanned aerial vehicles (UAVs) combined with emerging high‐performance sensor(s) have been intensively researched. Here, we present an aerial bridge surface survey method that can be used to assess damage. Existing damage detection methods focus on single types of damage and are limited in locating global damage, whereas our method detects two types of damage on the surface and marks them in a panoramic image. The workflow involves three steps: data acquisition using a meticulous UAV flight strategy that covers the entire surface, data processing using image‐based and point‐cloud models after polynomial rectification, and data output (i.e., damage detected by the combined models). To verify the method, a field test detected damage to two real bridges. A UAV equipped with a camera and light detection and ranging (LiDAR) equipment was employed. Experiments demonstrate the effectiveness of the proposed method, which is capable of producing accurate outputs and detecting damage with an average position error of 13.37 mm and a relative size error of 25.25%. Owing to the data fusion model taking advantage of two‐dimensional (2D) images and 3D LiDAR data, it outputs a high‐resolution 3D model and avoids environmental disturbances. After decision‐making‐level damage fusion, all position and size properties of damage information are computed into a panoramic damage image. This panoramic image showing all detecting damage helps technicians perform maintenance; the image can be zoomed to focus on any issue individually.