article Open AccessTop 1% cited
Comparison of deep convolutional neural networks and edge detectors for image-based crack detection in concrete
Construction and Building Materials · 2018 · Vol. 186 · pp. 1031–1045
Sattar Dorafshan✉(Utah State University)Robert J. Thomas(Clarkson University)Marc Maguire(Utah State University)
Infrastructure Maintenance and MonitoringConcrete Corrosion and DurabilityGeophysical Methods and ApplicationsConvolutional neural networkPrewitt operatorBlob detectionArtificial intelligenceSobel operatorComputer sciencePattern recognition (psychology)Canny edge detectorDetectorEdge detection
Citations
709
FWCI
49.75
field-weighted impact
References
62
Percentile
100%
vs. same field & year
Citations per year
Cited by
A systematic review of convolutional neural network-based structural condition assessment techniques
Engineering Structures · 2020 · 371 citations
Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review
Sensors · 2020 · 657 citations
Surface Defect Detection Methods for Industrial Products: A Review
Applied Sciences · 2021 · 374 citations
References
Deep learning in neural networks: An overview
Neural Networks · 2014 · 17,774 citations
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
IEEE Transactions on Medical Imaging · 2016 · 5,704 citations
Learning without Forgetting
IEEE Transactions on Pattern Analysis and Machine Intelligence · 2017 · 3,673 citations
NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion
IEEE Transactions on Industrial Electronics · 2017 · 902 citations
Citation Network
How this paper connects to the literature. Drag to explore, click any node to open that paper.