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Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks

2017· article· en· 3,134 citations· W2598457882 on OpenAlex· 10.1111/mice.12263

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Canadian affiliationAn author listed a Canadian institution. This is the only route the usual frame has.
Canadian funderA Canadian agency funded it. The work may carry no Canadian affiliation at all.

Abstract

No abstract. This is not a gap in this database — OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

The record

Venue
Computer-Aided Civil and Infrastructure Engineering
Topic
Infrastructure Maintenance and Monitoring
Field
Engineering
Canadian institutions
University of Manitoba
Funders
Natural Sciences and Engineering Research Council of Canada
Keywords
Convolutional neural networkSobel operatorComputer scienceArtificial intelligenceRobustness (evolution)PixelComputer visionCanny edge detectorShadow (psychology)Deep learningPattern recognition (psychology)AdaptabilityImage (mathematics)Edge detectionImage processing
Has abstract in OpenAlex
no