Automated steel bridge coating rust defect recognition method based on U-net fully convolutional networks
Journal
2nd IEEE International Conference on Architecture, Construction, Environment and Hydraulics 2020, ICACEH 2020
Pages
18-21
Date Issued
2020
Author(s)
Abstract
Nowadays, bridges are significant infrastructure in most countries, and it is crucial to come up with an effective corrosion detection method for steel bridge inspection. A crucial issue on rust recognition is to distinguish real rust corrosion spots and areas. A fully convolutional neural network, namely U-Net, is explored to develop an image semantic segmentation model, which provides a wide range of rust image recognition. ? 2020 IEEE.
Subjects
Convolution; Hydraulics; Image recognition; Image segmentation; Network architecture; Semantics; Steel bridges; Steel corrosion; Convolutional networks; Corrosion detection; Corrosion spots; Image semantics; Rust defects; Steel bridge coatings; Steel bridge inspections; Convolutional neural networks
SDGs
Type
conference paper