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Automatic Crack Detection Using Weakly Supervised Semantic Segmentation Network and Mixed-Label Training Strategy
- Source :
- Foundations of Computing and Decision Sciences, Vol 49, Iss 1, Pp 95-118 (2024)
- Publication Year :
- 2024
- Publisher :
- Sciendo, 2024.
-
Abstract
- Automatic crack detection in construction facilities is a challenging yet crucial task. However, existing deep learning (DL)-based semantic segmentation methods for this field are based on fully supervised learning models and pixel-level manual annotation, which are time-consuming and labor-intensive. To solve this problem, this paper proposes a novel crack semantic segmentation network using weakly supervised approach and mixed-label training strategy. Firstly, an image patch-level classifier of crack is trained to generate a coarse localization map for automatic pseudo-labeling of cracks combined with a thresholding-based method. Then, we integrated the pseudo-annotated with manual-annotated samples with a ratio of 4:1 to train the crack segmentation network with a mixed-label training strategy, in which the manual labels were assigned with a higher weight value. The experimental data on two public datasets demonstrate that our proposed method achieves a comparable accuracy with the fully supervised methods, reducing over 65% of the manual annotation workload.
Details
- Language :
- English
- ISSN :
- 23003405 and 20240007
- Volume :
- 49
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Foundations of Computing and Decision Sciences
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.62317abc42c14c2caa3cf16131515499
- Document Type :
- article
- Full Text :
- https://doi.org/10.2478/fcds-2024-0007