Annotated image database of cracked/uncracked concrete - Enabling AI to recognize cracks in concrete structures
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Examensarbete för masterexamen
Programme
Model builders
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Abstract
The development of mobile and handheld 3D scanners and small automated airborne vehicles
have opened new possibilities for structural health monitoring process. Digital Twin serves as a
living digital model of the real structure. Providing structural performance prediction by
processing varieties of data collected by sensors and inspections. To develop the subroutines for
crack detection and damage quantification, an annotated 3D point cloud database is needed. The
purpose of this work is to expand the available database of annotated 3D point cloud data of
cracked/uncracked concrete surfaces. To increase database diversity from mainly laboratory
specimens, only cracks from real structures in use are collected. It is suggested to test crack
detection tool on a 3D surface to compare the difference between the 2D and 3D convolutional
neural network. Most subroutine development for the crack detection tool has been for 2D.
A method to collect more examples is described in this thesis and works reasonably well.
The database consists of 61 annotated images with the average cracked/uncracked ratio of 3,1%
were collected. That corresponds to 182 subpictures per annotated image with the size of 32×32
pixels. It is not possible to annotate the images without having some subpictures falsely annotated.
Most of the incorrectly annotations are cracked subpictures annotated as uncracked. If a higher
accuracy is requested, there is a possibility to filter out most of the falsely annotated subpictures.
Most of the ambiguous and inaccurate annotations are positioned next to a subpicture annotated
as cracked. Removal of subpictures before training of the convolutional neural network are most
likely necessary due to the excess of uncracked concrete surface. Filter out all subpictures
annotated as uncracked and positioned next to a subpicture annotated as cracked will eliminate
many ambiguous pixels and most of the incorrectly annotated subpictures.
It is suggested to test the crack detection tool on 3D point cloud surfaces. The database was
partially built with the purpose to compare the differences using the results of the crack
detection tool for a 2D and a 3D Convolutional Neural Network.
Description
Keywords
Digital Twin, Annotated Image Database, NetworkCrack Detection, 3D Convolution