Abstract
During the long-term operation of hydro-junction infrastructure, water flow erosion causes concrete surfaces to crack, resulting in seepage, spalling, and rebar exposure. To ensure infrastructure safety, detecting such damage is critical. We propose a highly accurate damage detection method using a deep convolutional neural network with transfer learning. First, we collected images from hydro-junction infrastructure using a high-definition camera. Second, we preprocessed the images using an image expansion method. Finally, we modified the structure of Inception-v3 and trained the network using transfer learning to detect damage. The experiments show that the accuracy of the proposed damage detection method is 96.8%, considerably higher than the accuracy of a support vector machine. The results demonstrate that our damage detection method achieves better damage detection performance.
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The work described in this paper was supported by the Sichuan Energy Internet Research Center of Tsinghua University.
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Feng, C., Zhang, H., Wang, S. et al. Structural Damage Detection using Deep Convolutional Neural Network and Transfer Learning. KSCE J Civ Eng 23, 4493–4502 (2019). https://doi.org/10.1007/s12205-019-0437-z
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DOI: https://doi.org/10.1007/s12205-019-0437-z