PointCrack3D: Crack Detection in Unstructured Environments using a
3D-Point-Cloud-Based Deep Neural Network
- URL: http://arxiv.org/abs/2111.11615v1
- Date: Tue, 23 Nov 2021 02:33:18 GMT
- Title: PointCrack3D: Crack Detection in Unstructured Environments using a
3D-Point-Cloud-Based Deep Neural Network
- Authors: Faris Azhari and Charlotte Sennersten and Michael Milford and Thierry
Peynot
- Abstract summary: This paper presents PointCrack3D, a new 3D-point-cloud-based crack detection algorithm for unstructured surfaces.
The method was validated experimentally on a new large natural rock dataset.
Results demonstrate a crack detection rate of 97% overall and 100% for cracks with a maximum width of more than 3 cm.
- Score: 20.330700719146215
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Surface cracks on buildings, natural walls and underground mine tunnels can
indicate serious structural integrity issues that threaten the safety of the
structure and people in the environment. Timely detection and monitoring of
cracks are crucial to managing these risks, especially if the systems can be
made highly automated through robots. Vision-based crack detection algorithms
using deep neural networks have exhibited promise for structured surfaces such
as walls or civil engineering tunnels, but little work has addressed highly
unstructured environments such as rock cliffs and bare mining tunnels. To
address this challenge, this paper presents PointCrack3D, a new
3D-point-cloud-based crack detection algorithm for unstructured surfaces. The
method comprises three key components: an adaptive down-sampling method that
maintains sufficient crack point density, a DNN that classifies each point as
crack or non-crack, and a post-processing clustering method that groups crack
points into crack instances. The method was validated experimentally on a new
large natural rock dataset, comprising coloured LIDAR point clouds spanning
more than 900 m^2 and 412 individual cracks. Results demonstrate a crack
detection rate of 97% overall and 100% for cracks with a maximum width of more
than 3 cm, significantly outperforming the state of the art. Furthermore, for
cross-validation, PointCrack3D was applied to an entirely new dataset acquired
in different locations and not used at all in training and shown to detect 100%
of its crack instances. We also characterise the relationship between detection
performance, crack width and number of points per crack, providing a foundation
upon which to make decisions about both practical deployments and future
research directions.
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