Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection

Gopalakrishnan, Kasthurirangan; Khaitan, Siddhartha K.; Choudhary, Alok; Agrawal, Ankit

doi:10.1016/j.conbuildmat.2017.09.110

Title: Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection

Full Record
Other Related Research

Authors:

; Khaitan, Siddhartha K.; Choudhary, Alok; Agrawal, Ankit

Publication Date:: 2017-12-01

Sponsoring Org.:: USDOE

OSTI Identifier:: 1549213

Grant/Contract Number:: SC0007456; SC0014330

Resource Type:: Publisher's Accepted Manuscript

Journal Name:: Construction and Building Materials

Additional Journal Information:: Journal Name: Construction and Building Materials Journal Volume: 157 Journal Issue: C; Journal ID: ISSN 0950-0618

Publisher:: Elsevier

Country of Publication:: United Kingdom

Language:: English

Citation Formats


                    Gopalakrishnan, Kasthurirangan, Khaitan, Siddhartha K., Choudhary, Alok, and Agrawal, Ankit. Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection.  United Kingdom: N. p., 2017. 
Web.  doi:10.1016/j.conbuildmat.2017.09.110.

Copy to clipboard


                    Gopalakrishnan, Kasthurirangan, Khaitan, Siddhartha K., Choudhary, Alok, & Agrawal, Ankit. Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection.  United Kingdom.  https://doi.org/10.1016/j.conbuildmat.2017.09.110

Copy to clipboard


                    Gopalakrishnan, Kasthurirangan, Khaitan, Siddhartha K., Choudhary, Alok, and Agrawal, Ankit. Fri .  
"Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection".  United Kingdom.  https://doi.org/10.1016/j.conbuildmat.2017.09.110.

Copy to clipboard


                    
@article{osti_1549213,

  title        = {Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection},

  author       = {Gopalakrishnan, Kasthurirangan and Khaitan, Siddhartha K. and Choudhary, Alok and Agrawal, Ankit},

  abstractNote = {},

  doi          = {10.1016/j.conbuildmat.2017.09.110},

  journal      = {Construction and Building Materials},

  number       = C,

  volume       = 157,

  place        = {United Kingdom},

  year         = {2017},

  month        = {12}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (Publisher)

Publisher's Version of Record
https://doi.org/10.1016/j.conbuildmat.2017.09.110

Other availability

Search WorldCat to find libraries that may hold this journal

Citation Metrics:

Cited by: 113 works

Citation information provided by
Web of Science

Save / Share:

Export Metadata

Save to My Library

Similar Records in DOE PAGES and OSTI.GOV collections:

Extracting Grain Orientations from EBSD Patterns of Polycrystalline Materials Using Convolutional Neural Networks

Journal Article Jha, Dipendra ; Singh, Saransh ; Al-Bahrani, Reda ; ... - Microscopy and Microanalysis

Abstract We present a deep learning approach to the indexing of electron backscatter diffraction (EBSD) patterns. We design and implement a deep convolutional neural network architecture to predict crystal orientation from the EBSD patterns. We design a differentiable approximation to the disorientation function between the predicted crystal orientation and the ground truth; the deep learning model optimizes for the mean disorientation error between the predicted crystal orientation and the ground truth using stochastic gradient descent. The deep learning model is trained using 374,852 EBSD patterns of polycrystalline nickel from simulation and evaluated using 1,000 experimental EBSD patterns of polycrystalline nickel.more »« less
https://doi.org/10.1017/s1431927618015131
Bridging the Gap between Laboratory and Field Experiments in American Eel Detection Using Transfer Learning and Convolutional Neural Network

Conference Yin, Tianzhixi ; Zang, Xiaoqin ; Hou, Zhangshuan ; ...

An automatic system that utilizes data analytics and machine learning to identify adult American eel in data obtained by imaging sonars is created in this study. Wavelet transform has been applied to de-noise the sonar data and a convolutional neural network model has been built to classify eels and non-eel objects. Because of the unbalanced amounts of data in laboratory and field experiments, a transfer learning strategy is implemented to fine-tune the convolutional neural network model so that it performs well for both the laboratory and field data. The proposed system can provide important information to develop mitigation strategies formore »« less
https://doi.org/10.24251/HICSS.2020.116
Bridging the Gap between Laboratory and Field Experiments in American Eel Detection Using Transfer Learning and Convolutional Neural Network

Journal Article Yin, Tianzhixi ; Zang, Xiaoqin ; Hou, Zhangshuan ; ... - Proceedings of the 53rd Hawaii International Conference on System Sciences

An automatic system that utilizes data analytics and machine learning to identify adult American eel in data obtained by imaging sonars is created in this study. Wavelet transform has been applied to de-noise the ARIS sonar data and a convolutional neural network model has been built to classify eels and non-eel objects. Because of the unbalanced amounts of data in laboratory and field experiments, a transfer learning strategy is implemented to fine-tune the convolutional neural network model so that it performs well for both the laboratory and field data. The proposed system can provide important information to develop mitigation strategiesmore »« less
https://doi.org/10.24251/hicss.2020.116

Full Text Available
Converting tabular data into images for deep learning with convolutional neural networks

Journal Article Zhu, Yitan ; Brettin, Thomas ; Xia, Fangfang ; ... - Scientific Reports

AbstractConvolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume a spatial relationship between features, and thus are unsuitable for modeling using CNNs. To meet this challenge, we develop a novel algorithm, image generator for tabular data (IGTD), to transform tabular data into images by assigning features to pixel positions so that similar features are close to each other in the image. The algorithm searches for an optimized assignment by minimizing the difference between themore »« less
https://doi.org/10.1038/s41598-021-90923-y
A data-driven CO₂ leakage detection using seismic data and spatial–temporal densely connected convolutional neural networks

Journal Article Zhou, Zheng ; Lin, Youzuo ; Zhang, Zhongping ; ... - International Journal of Greenhouse Gas Control

In carbon capture and sequestration (also known as carbon capture and storage, or CCS), developing effective monitoring methods is needed to detect and respond to CO₂ leakage. CO₂ leakage detection methods rely on geophysical observations and monitoring sensor network. However, traditional methods usually require the development of site-specific physical models and expert interpretation, and the effectiveness of these methods can be limited to different application locations, operational scenarios, and conditions. Here, we developed a novel data-driven leakage detection method based on densely connected convolutional neural networks. Our method is an end-to-end detection approach, that differs from conventional leakage monitoring methodsmore »« less
https://doi.org/10.1016/j.ijggc.2019.102790

Full Text Available

Similar Records