Semi‐supervised learning based on convolutional neural network and uncertainty filter for façade defects classification
Author:
Affiliation:
1. Department of Building, School of Design and Environment National University of Singapore Singapore Singapore
2. Department of Electrical and Computer Engineering, Faculty of Engineering National University of Singapore Singapore Singapore
Publisher
Wiley
Subject
Computational Theory and Mathematics,Computer Graphics and Computer-Aided Design,Computer Science Applications,Civil and Structural Engineering
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1111/mice.12632
Reference54 articles.
1. Combining labeled and unlabeled data with co-training
2. Semi‐supervised multiresolution classification using adaptive graph filtering with application to indirect bridge structural health monitoring;Chen S.;IEEE Transactions on Signal Processing,2014
3. Automated detection of sewer pipe defects in closed-circuit television images using deep learning techniques
4. Maintainability of Facilities
5. Road surface damage detection using fully convolutional neural networks and semi‐supervised learning;Chun C.;Sensors (Basel, Switzerland),2019
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