Detection Of Concrete Cracks using Dual-channel Deep Convolutional
Network
- URL: http://arxiv.org/abs/2009.10612v1
- Date: Tue, 22 Sep 2020 15:17:02 GMT
- Title: Detection Of Concrete Cracks using Dual-channel Deep Convolutional
Network
- Authors: Babloo Kumar and Sayantari Ghosh
- Abstract summary: This study proposes a crack detection method based on deep convolutional neural network (CNN) for detection of concrete cracks.
A database of 3200 labelled images with concrete cracks has been created, where the contrast, lighting conditions, orientations and severity of the cracks were extremely variable.
We have designed a dual-channel deep CNN which shows high accuracy ( 92.25%) as well as robustness in finding concrete cracks in realis-tic situations.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Due to cyclic loading and fatigue stress cracks are generated, which affect
the safety of any civil infrastructure. Nowadays machine vision is being used
to assist us for appropriate maintenance, monitoring and inspection of concrete
structures by partial replacement of human-conducted onsite inspections. The
current study proposes a crack detection method based on deep convolutional
neural network (CNN) for detection of concrete cracks without explicitly
calculating the defect features. In the course of the study, a database of 3200
labelled images with concrete cracks has been created, where the contrast,
lighting conditions, orientations and severity of the cracks were extremely
variable. In this paper, starting from a deep CNN trained with these images of
256 x 256 pixel-resolution, we have gradually optimized the model by
identifying the difficulties. Using an augmented dataset, which takes into
account the variations and degradations compatible to drone videos, like,
random zooming, rotation and intensity scaling and exhaustive ablation studies,
we have designed a dual-channel deep CNN which shows high accuracy (~ 92.25%)
as well as robustness in finding concrete cracks in realis-tic situations. The
model has been tested on the basis of performance and analyzed with the help of
feature maps, which establishes the importance of the dual-channel structure.
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