DeepCrack: A deep hierarchical feature learning architecture for crack segmentation
Top Cited Papers
- 1 April 2019
- journal article
- research article
- Published by Elsevier BV in Neurocomputing
- Vol. 338, 139-153
- https://doi.org/10.1016/j.neucom.2019.01.036
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (41571436, 91438203)
- National Natural Science Foundation of China
This publication has 29 references indexed in Scilit:
- Automatic Crack Detection and Classification Method for Subway Tunnel Safety MonitoringSensors, 2014
- The Pascal Visual Object Classes Challenge: A RetrospectiveInternational Journal of Computer Vision, 2014
- CrackTree: Automatic crack detection from pavement imagesPattern Recognition Letters, 2012
- FoSA: F* Seed-growing Approach for crack-line detection from pavement imagesImage and Vision Computing, 2011
- Automatic Road Pavement Assessment with Image Processing: Review and ComparisonInternational Journal of Geophysics, 2011
- A Novel LBP Based Methods for Pavement Crack DetectionJournal of Pattern Recognition Research, 2010
- The Pascal Visual Object Classes (VOC) ChallengeInternational Journal of Computer Vision, 2009
- Wavelet-based pavement distress detection and evaluationOptical Engineering, 2006
- Measure of texture anisotropy for crack detection on textured surfacesElectronics Letters, 1996
- Receptive fields, binocular interaction and functional architecture in the cat's visual cortexJournal Of Physiology-London, 1962