A deeper generative adversarial network for grooved cement concrete pavement crack detection
Published 2023 View Full Article
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Title
A deeper generative adversarial network for grooved cement concrete pavement crack detection
Authors
Keywords
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Journal
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Volume 119, Issue -, Pages 105808
Publisher
Elsevier BV
Online
2023-01-05
DOI
10.1016/j.engappai.2022.105808
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- (2021) Deeksha Arya et al. AUTOMATION IN CONSTRUCTION
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- (2020) Yuchuan Du et al. International Journal of Pavement Engineering
- Pavement Image Datasets: A New Benchmark Dataset to Classify and Densify Pavement Distresses
- (2020) Hamed Majidifard et al. TRANSPORTATION RESEARCH RECORD
- CrackU-net: A novel deep convolutional neural network for pixelwise pavement crack detection
- (2020) Ju Huyan et al. Structural Control & Health Monitoring
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- (2020) Hiroya Maeda et al. COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
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- (2020) Ian Goodfellow et al. COMMUNICATIONS OF THE ACM
- A Novel Approach to Data Augmentation for Pavement Distress Segmentation
- (2020) Davide Mazzini et al. COMPUTERS IN INDUSTRY
- Machine Learning for Crack Detection: Review and Model Performance Comparison
- (2020) Yung-An Hsieh et al. JOURNAL OF COMPUTING IN CIVIL ENGINEERING
- CrackGAN: Pavement Crack Detection Using Partially Accurate Ground Truths Based on Generative Adversarial Learning
- (2020) Kaige Zhang et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Faster region convolutional neural network for automated pavement distress detection
- (2019) Liang Song et al. Road Materials and Pavement Design
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- (2018) Qin Zou et al. IEEE TRANSACTIONS ON IMAGE PROCESSING
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- Evaluating Pavement Cracks with Bidimensional Empirical Mode Decomposition
- (2008) Albert Ayenu-Prah et al. EURASIP Journal on Advances in Signal Processing
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