Novel Spatio-Temporal Joint Learning-Based Intelligent Hollowing Detection in Dams for Low-Data Infrared Images.

Journal: Sensors (Basel, Switzerland)
Published Date: May 19, 2025

Abstract

Concrete dams are prone to various hidden dangers after long-term operation and may lead to significant risk if failed to be detected in time. However, the existing hollowing detection techniques are few as well as inefficient when facing the demands of comprehensive coverage and intelligent management for regular inspections. Hence, we proposed an innovative, non-destructive infrared inspection method via constructed dataset and proposed deep learning algorithms. We first modeled the surface temperature field variation of concrete dams as a one-dimensional, non-stationary partial differential equation with Robin boundary. We also designed physics-informed neural networks (PINNs) with multi-subnets to compute the temperature value automatically. Secondly, we obtained the time-domain features in one-dimensional space and used the diffusion techniques to obtain the synthetic infrared images with dam hollowing by converting the one-dimensional temperatures into two-dimensional ones. Finally, we employed adaptive joint learning to obtain the spatio-temporal features. We designed the experiments on the dataset we constructed, and we demonstrated that the method proposed in this paper can handle the low-data (few shots real images) issue. Our method achieved 94.7% of recognition accuracy based on few shots real images, which is 17.9% and 5.8% higher than maximum entropy and classical OTSU methods, respectively. Furthermore, it attained a sub-10% cross-sectional calculation error for hollowing dimensions, outperforming maximum entropy (70.5% error reduction) and OTSU (7.4% error reduction) methods, which shows our method being one novel method for automated intelligent hollowing detection.

Authors

  • Lili Zhang
    Pharmaceutics Department, Institute of Medicinal Biotechnology, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, 100050, PR China.
  • Zihan Jin
    College of Information Science and Engineering, Hohai University, Changzhou 213200, China.
  • Yibo Wang
    Dosage Form Design and Development, BioPharmaceuticals Development, R&D, AstraZeneca, Gaithersburg, MD, USA.
  • Ziyi Wang
    College of Science, Beijing Forestry University, Beijing, China.
  • Zeyu Duan
    College of Information Science and Engineering, Hohai University, Changzhou 213200, China.
  • Taoran Qi
    College of Information Science and Engineering, Hohai University, Changzhou 213200, China.
  • Rui Shi
    From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, 2nd Anzhen Road, Chaoyang District, Beijing, China (N.Z., L.X., Z.F.); Cardiovascular Research Centre, Royal Brompton Hospital, London, England (G.Y., R.S., J.K., D.F.); National Heart and Lung Institute, Imperial College London, London, England (G.Y., R.S., J.K., D.F.); Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China (Z.G., H.Z.); Anhui University, Hefei, China (C.X., Y.Z.); and School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, China (H.Z.).

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