Abstract
For the inspection of structures, particularly bridges, it is becoming common to replace humans with autonomous systems that use unmanned aerial vehicles (UAV). In this paper, a framework for autonomous bridge inspection using a UAV is proposed with a four-step workflow: (a) data acquisition with an efficient UAV flight path, (b) computer vision comprising training, testing and validation of convolutional neural networks (ConvNets), (c) point cloud generation using intelligent hierarchical dense structure from motion (DSfM), and (d) damage quantification. This workflow starts with planning the most efficient flight path that allows for capturing of the minimum number of images required to achieve the maximum accuracy for the desired defect size, then followed by bridge and damage recognition. Three types of autonomous detection are used: masking the background of the images, detecting areas of potential damage, and pixel-wise damage segmentation. Detection of bridge components by masking extraneous parts of the image, such as vegetation, sky, roads or rivers, can improve the 3D reconstruction in the feature detection and matching stages. In addition, detecting damaged areas involves the UAV capturing close-range images of these critical regions, and damage segmentation facilitates damage quantification using 2D images. By application of DSfM, a denser and more accurate point cloud can be generated for these detected areas, and aligned to the overall point cloud to create a digital model of the bridge. Then, this generated point cloud is evaluated in terms of outlier noise, and surface deviation. Finally, damage that has been detected is quantified and verified, based on the point cloud generated using the Terrestrial Laser Scanning (TLS) method. The results indicate this workflow for autonomous bridge inspection has potential.
Highlights
All the information collected during regular inspections can be used as input data to a Bridge Management Systems (BMSs), such as BaTMan in Sweden, which reports the condition of structural members of a bridge
Based on the knowledge obtained from our experiences, from both computer vision and point cloud generation work, we conclude: 1
Comparisons of semantic segmentation for both pixel-wise bridge components and damage detection show that U-Net performs better for joint gap segmentation, while SegNet is more efficient with large-scale objects, and so is better at bridge component detection; Image normalization and augmentation expand the diversity of the generated dataset by involving random rescaling, horizontal flips, changes to brightness, contrast, and color, as well as random cropping
Summary
Bridges are an essential part of transportation infrastructure, and it is necessary to monitor their operation during their service life. To ensure the safety of a bridge, regular inspections are required to detect areas that may have become damaged. A measurement regime can be implemented to provide a proactive approach to maintenance services. All the information collected during regular inspections can be used as input data to a Bridge Management Systems (BMSs), such as BaTMan in Sweden, which reports the condition of structural members of a bridge. The process of regular inspection, coupled with performance analysis strategies, for engineering structures is defined as Structural Health
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