LFFNet: Layered feature fusion network enhanced passive infrared structural analysis in large-scale geomembrane covers
Yue Ma, Wenhao Huang, Benjamin Steven Vien, Thomas Kuen, Wing Kong Chiu
Abstract. Anaerobic lagoons at sewage treatment plants are covered with multiple sheets of high-density polyethylene (HDPE) geomembranes to prevent the emission of odorous gases and to harness biogas as a renewable energy source. Over time, raw sewage can accumulate and solidify into a mass, which can deform the covers and potentially affect their structural integrity. Currently, traditional passive thermography is limited by insufficient thermal excitations and has difficulty identifying features and substances beneath the covers. This study proposes a novel segmentation model, Layered Feature Fusion Network (LFFNet), to identify structural anomalies on large-scale geomembranes in sewage treatment plants by integrating infrared imaging and deep learning techniques. Notably, the model performs exceptionally well in addressing class imbalance issues, reaching a maximum Mean Intersection over Union (mIoU) of 94.68% and a maximum Mean Pixel Accuracy (mPA) of 97.49%.
Keywords
Thermal Image, Image Segmentation, Structural Health Monitoring, Deep Learning, Sewage Treatment
Published online 3/25/2025, 9 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Yue Ma, Wenhao Huang, Benjamin Steven Vien, Thomas Kuen, Wing Kong Chiu, LFFNet: Layered feature fusion network enhanced passive infrared structural analysis in large-scale geomembrane covers, Materials Research Proceedings, Vol. 50, pp 180-188, 2025
DOI: https://doi.org/10.21741/9781644903513-22
The article was published as article 22 of the book Structural Health Monitoring
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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