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Workflow for Off-Site Bridge Inspection Using Automatic Damage Detection-Case Study of the Pahtajokk Bridge by Ali Mirzazade; Cosmin Popescu; Thomas Blanksvärd; Björn Täljsten is a Environmental Science article available to read on EtoBox.

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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 involve

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Author

Ali Mirzazade; Cosmin Popescu; Thomas Blanksvärd; Björn Täljsten

Publisher

MDPI AG

Published

2021

Language

EN

Field

Environmental Science (Physical Sciences)