dc.contributor
Kokuritsu Jōhōgaku Kenkyūjo
dc.contributor
Prendinger, Helmut
dc.contributor
Escalera Guerrero, Sergio
dc.contributor.author
Rubio Guillamón, Juanjo
dc.date.issued
2018-04-16
dc.identifier
https://hdl.handle.net/2117/118758
dc.description.abstract
Fully Convolutional Networks prove to be suitable method for texture-based damage segmentation on infrastructure. A dataset has been collected to model the uncertainty in human inspection of bridges in the Japanese prefecture of Niigata.
dc.format
application/pdf
dc.publisher
Universitat Politècnica de Catalunya
dc.subject
Àrees temàtiques de la UPC::Informàtica
dc.subject
Neural networks (Computer science)
dc.subject
Machine learning
dc.subject
infrastructure
dc.subject
civil engineering
dc.subject
neural networks
dc.subject
convolutional neural networks
dc.subject
segmentacio semantic
dc.subject
infrastructura
dc.subject
enginyeria civil
dc.subject
xarxes neuronals
dc.subject
semantic segmentation
dc.subject
Xarxes neuronals (Informàtica)
dc.subject
Aprenentatge automàtic
dc.title
Bridge Structural Damage Segmentation Using Fully Convolutional Networks
dc.title
Deep learning for infraestructure damage categorization
