- Title
- Real-time concrete crack detection and instance segmentation using deep transfer learning
- Creator
- Piyathilaka, Lasitha; Preethichandra, Daluwathu; Izhar, Umer; Appuhamillage, Gayan
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/198277
- Identifier
- vital:19018
- Identifier
- ISSN:2673-4591
- Identifier
-
https://doi.org/10.3390/ecsa-7-08260
- Abstract
- Cracks on concrete infrastructure are one of the early indications of structural degradation which needs to be identified early as possible to carry out early preventive measures to avoid further damage. In this paper, we propose to use YOLACT: a real-time instance segmentation algorithm for automatic concrete crack detection. This deep learning algorithm is used with transfer learning to train the YOLACT network to identify and localize cracks with their corresponding masks which can be used to identify each crack instance. The transfer learning techniques allowed us to train the network on a relatively small dataset of 500 crack images. To train the YOLACT network, we created a dataset with ground-truth masks from images collected from publicly available datasets. We evaluated the trained YOLACT model for concrete crack detection with ResNet-50 and ResNet-101 backbone architectures for both precision and speed of detection. The trained model achieved high mAP results with real-time frame rates when tested on concrete crack images on a single GPU. The YOLACT algorithm was able to correctly segment multiple cracks with individual instance level masks with high localization accuracy.
- Publisher
- MDPI
- Relation
- Engineering Proceedings Vol. 2, no. 1 (2020), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright by the authors
- Rights
- Open Access
- Subject
- Crack detection; Visual inspection; Infrastructure monitoring; Deep learning; Real-time detection; Crack dataset; 40 Engineering
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