Automatic Crack Detection Using Weakly Supervised Semantic Segmentation Network and Mixed-Label Training Strategy

• Autorzy: Zhang Shuyuan , Xu Hongli , Zhu Xiaoran , Xie Lipeng

Identyfikatory

DOI

10.2478/fcds-2024-0007

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

weakly supervised learning; crack detection; semantic segmentation; mixed label

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2024
• Tom: Vol. 49, No. 1
• Strony: 95--118
• Opis fizyczny: Bibliogr. 36 poz., rys., tab.

Twórcy

autor

Zhang Shuyuan

• School of Cyber Science and Engineering Zhengzhou University, Zhengzhou, China

autor

Xu Hongli

• School of Cyber Science and Engineering Zhengzhou University, Zhengzhou, China

autor

Zhu Xiaoran

• Hanwei Electronics Group Corporation, Zhengzhou, China

autor

Xie Lipeng

• Hanwei Electronics Group Corporation, Zhengzhou, China

Bibliografia

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