Enhancing bridge defect semantic segmentation via synthetic point cloud data generation

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Xu, Yuansheng and Jesus, Andre and Hancock, Craig and Wang, Mingzhu; Moreno-Rangel, Alejandro and Kumar, Bimal, eds. (2025) Enhancing bridge defect semantic segmentation via synthetic point cloud data generation. In: EG-ICE 2025. University of Strathclyde Publishing, GBR, pp. 183-190. ISBN 9781914241826 (https://doi.org/10.17868/strath.00093232)

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Abstract

The structural integrity of bridges is critical for modern transportation systems, yet current methods for defect detection and segmentation on curved concrete surfaces remain limited in precision and cost-effectiveness. Data scarcity is also a significant problem. This study addresses these challenges by proposing a framework leveraging synthetic data generation and a Surface Normal Enhanced PointConv (SNEPointConv) model for bridge defect semantic segmentation. The proposed approach includes a low-cost method for generating synthetic cracks and spalling defects that mimic real-world bridge defect geometries, enabling effective training of deep learning models. Additionally, the SNEPointConv model integrates normal vector enhancements to improve feature extraction from irregular point clouds. Experiments demonstrate the feasibility of using synthetic datasets for defect semantic segmentation, bridging the gap between real-world defect characteristics and digital twin applications for structural health monitoring. Key contributions include the development of scalable synthetic defect data generation techniques, improved defect segmentation accuracy through feature enhancement, and a comprehensive exploration of the effectiveness of synthetic dataset in model training.

  • Item type:Book Section
    ID code:93232
    Dates:
    Date
    Event
    1 July 2025
    Published
    9 June 2025
    Accepted
    Subjects:Fine Arts > Architecture
    Department:Faculty of Engineering > Architecture
    Depositing user: Pure Administrator
    Date deposited:26 Jun 2025 15:14
    Last modified:11 Apr 2026 00:53
    URI:https://strathprints.strath.ac.uk/id/eprint/93232
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