Journal
CONSTRUCTION AND BUILDING MATERIALS
Volume 323, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.conbuildmat.2022.126416
Keywords
Convolutional neural network; Dam inspection; Seepage defect; Unmanned aerial vehicles
Categories
Funding
- National Natural Science Foundation of China [41877230]
- Natural Science Foundation of Shandong Province [ZR2018MEE052]
- Shandong Provincial Key Research and Development Program (SPKRDP) [2019GGX101027]
- Natural Science Foundation of Guangdong Province [2021A1515011782]
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In this study, a novel convolutional neural network is proposed to automatically identify dam-surface seepage from low-resolution thermograms. The method achieves superior results by reducing false alarms caused by background interference and accurately identifying seepage profiles. Experimental results confirm the effectiveness of the proposed network.
Seepage inspections are vitally important for delineating damage zones and ensuring the long-term safe operation of dams. However, vegetation, complex backgrounds, and low signal-to-noise ratio and poor thermogram resolution impose significant adverse effects on automated results. In this study, a novel convolutional neural network is proposed to automatically identify dam-surface seepage from thermograms collected by an unmanned aerial vehicle carrying a thermal imaging camera. An auxiliary input branch with two specially designed modules (i.e., a region-of-key-temperature fusion unit and a convolutional block attention module) are added to a U-Net frame to reduce the false-alarm rate caused by seepage-like background interference on the dams and accurately identify seepage profiles with clear boundaries from the low-resolution thermograms. The method is utilized on actual dams, and experimental results confirm its superiority, even with interference. The Dice coefficient score and intersection-over-union metrics of the proposed network are 87.58 and 78.12 %, respectively: an increase of 3.67 and 4.80% over U-Net and 1.18 and 1.43 % over Mobile DeepLabv3. Under the same computing environment and dataset, the training time of the proposed network in this paper is 8 min and 57 s, U-Net is 3 min and 13 s, Mobile DeepLabv3 is 12 min and 44 s. This study provides a promising and cost-effective automatic dam-surface seepage inspection method.
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