A Weakly-Supervised Surface Crack Segmentation Method using Localisation
with a Classifier and Thresholding
- URL: http://arxiv.org/abs/2109.00456v2
- Date: Fri, 3 Sep 2021 08:46:11 GMT
- Title: A Weakly-Supervised Surface Crack Segmentation Method using Localisation
with a Classifier and Thresholding
- Authors: Jacob K\"onig, Mark Jenkins, Mike Mannion, Peter Barrie, Gordon
Morison
- Abstract summary: Recent work has been addressing this problem by supporting structural maintenance measures using machine learning methods.
Our work proposes a weakly supervised approach which leverages a CNN classifier to create surface crack segmentation maps.
We focus on the ease of implementation of our method and it is shown to perform well on several surface crack datasets.
- Score: 0.20999222360659603
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Surface cracks are a common sight on public infrastructure nowadays. Recent
work has been addressing this problem by supporting structural maintenance
measures using machine learning methods which segment surface cracks from their
background so that they are easy to localize. However, a common issue with
those methods is that to create a well functioning algorithm, the training data
needs to have detailed annotations of pixels that belong to cracks. Our work
proposes a weakly supervised approach which leverages a CNN classifier to
create surface crack segmentation maps. We use this classifier to create a
rough crack localisation map by using its class activation maps and a patch
based classification approach and fuse this with a thresholding based approach
to segment the mostly darker crack pixels. The classifier assists in
suppressing noise from the background regions, which commonly are incorrectly
highlighted as cracks by standard thresholding methods. We focus on the ease of
implementation of our method and it is shown to perform well on several surface
crack datasets, segmenting cracks efficiently even though the only data that
was used for training were simple classification labels.
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