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.
Structural Damage Detection using Deep Convolutional Neural Network and Transfer Learning
KSCE Journal of Civil Engineering ; 23 , 10 ; 4493-4502
2019-09-03
10 pages
Article (Journal)
Electronic Resource
English
Structural Damage Detection using Deep Convolutional Neural Network and Transfer Learning
Online Contents | 2019
|Emergency Vehicle Detection Using Deep Convolutional Neural Network
Springer Verlag | 2022
|Pedestrian Detection with Deep Convolutional Neural Network
British Library Conference Proceedings | 2015
|