Deep learning for infraestructure damage categorization
Rubio Guillamón, Juanjo
Kokuritsu Jōhōgaku Kenkyūjo
Prendinger, Helmut
Escalera Guerrero, Sergio
2018-04-16
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.
Master thesis
Anglès
Àrees temàtiques de la UPC::Informàtica; Neural networks (Computer science); Semantics; Machine learning; infrastructure; civil engineering; neural networks; convolutional neural networks; segmentacio semantic; infrastructura; enginyeria civil; xarxes neuronals; semantic segmentation; Xarxes neuronals (Informàtica); Semàntica; Aprenentatge automàtic
Universitat Politècnica de Catalunya
DIDL MARC MARC_CCUC METS OAI_DC ORE QDC RDF
Open Access
Treballs acadèmics [82541]
