Unsupervised Learning of Pavement Distresses from Surface Images
- 24 July 2021
- book chapter
- conference paper
- Published by Springer Science and Business Media LLC
Abstract
No abstract availableKeywords
This publication has 12 references indexed in Scilit:
- Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping SystemApplied Sciences, 2019
- Image Processing–Based Classification of Asphalt Pavement Cracks Using Support Vector Machine Optimized by Artificial Bee ColonyJournal of Computing in Civil Engineering, 2018
- Road Damage Detection and Classification Using Deep Neural Networks with Smartphone ImagesComputer-Aided Civil and Infrastructure Engineering, 2018
- Deep Learning‐Based Crack Damage Detection Using Convolutional Neural NetworksComputer-Aided Civil and Infrastructure Engineering, 2017
- Road crack detection using deep convolutional neural networkPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2016
- CrackIT — An image processing toolbox for crack detection and characterizationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- Pavement crack detection using the Gabor filterPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2013
- Automatic Road Crack Detection and CharacterizationIEEE Transactions on Intelligent Transportation Systems, 2012
- CrackTree: Automatic crack detection from pavement imagesPattern Recognition Letters, 2012
- A Novel LBP Based Methods for Pavement Crack DetectionJournal of Pattern Recognition Research, 2010