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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