Crack Detection Based on Generative Adversarial Networks and Deep Learning

Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network

(2021) Shuai Teng et al.   Applied Sciences-Basel

Multi-objective wind power scenario forecasting based on PG-GAN

(2021) Ran Yuan et al.   ENERGY

Benefits of ensemble models in road pavement cracking classification

(2020) Francisco J. Rodriguez‐Lozano et al.   COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING

Self-Supervised Structure Learning for Crack Detection Based on Cycle-Consistent Generative Adversarial Networks

(2020) Kaige Zhang et al.   JOURNAL OF COMPUTING IN CIVIL ENGINEERING

An integrated approach to automatic pixel-level crack detection and quantification of asphalt pavement

(2020) Ankang Ji et al.   AUTOMATION IN CONSTRUCTION

Generative adversarial network for road damage detection

(2020) Hiroya Maeda et al.   COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING

A generative adversarial network for travel times imputation using trajectory data

(2020) Kunpeng Zhang et al.   COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING

Generative adversarial networks

(2020) Ian Goodfellow et al.   COMMUNICATIONS OF THE ACM

Automatic Pavement Type Recognition for Image-Based Pavement Condition Survey Using Convolutional Neural Network

(2020) Guangwei Yang et al.   JOURNAL OF COMPUTING IN CIVIL ENGINEERING

Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network

(2019) Shengyuan Li et al.   COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING

Encoder-decoder network for pixel-level road crack detection in black-box images

(2019) Seongdeok Bang et al.   COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING

Vibration‐based structural state identification by a 1‐dimensional convolutional neural network

(2019) Youqi Zhang et al.   COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING

Computer vision-based concrete crack detection using U-net fully convolutional networks

(2019) Zhenqing Liu et al.   AUTOMATION IN CONSTRUCTION

Novel data augmentation strategies to boost supervised segmentation of plant disease

(2019) Clément Douarre et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Structural damage detection using convolutional neural networks combining strain energy and dynamic response

(2019) Shuai Teng et al.   MECCANICA

Automated defect classification in sewer closed circuit television inspections using deep convolutional neural networks

(2018) Srinath S. Kumar et al.   AUTOMATION IN CONSTRUCTION

NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion

(2018) Fu-Chen Chen et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Automatic classification of pavement crack using deep convolutional neural network

(2018) Baoxian Li et al.   International Journal of Pavement Engineering

Automatic Pixel-Level Crack Detection and Measurement Using Fully Convolutional Network

(2018) Xincong Yang et al.   COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING

Autonomous concrete crack detection using deep fully convolutional neural network

(2018) Cao Vu Dung et al.   AUTOMATION IN CONSTRUCTION

Recognition of asphalt pavement crack length using deep convolutional neural networks

(2017) Zheng Tong et al.   Road Materials and Pavement Design

Multifractal analysis of crack patterns in reinforced concrete shear walls

(2016) Arvin Ebrahimkhanlou et al.   STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL

ImageNet Large Scale Visual Recognition Challenge

(2015) Olga Russakovsky et al.   INTERNATIONAL JOURNAL OF COMPUTER VISION

Deep learning

(2015) Yann LeCun et al.   NATURE

Automated assessment of cracks on concrete surfaces using adaptive digital image processing

(2014) Yufei Liu et al.   Smart Structures and Systems

A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures

(2008) Mohammad R. Jahanshahi et al.   Structure and Infrastructure Engineering