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0505
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Le Bastard, C.,
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0709
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1503
Ground penetrating radar
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Improved percolation-based method for crack detection in concrete
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Hansen, M.E.[Michael E.],
Ersbřll, B.K.[Bjarne K.],
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1103
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1109
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IVC(29), No. 12, November 2011, pp. 861-872.
Elsevier DOI
1112
Line detection; Pavement crack; Seed-growing; Dynamic programming
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PRL(33), No. 3, 1 February 2012, pp. 227-238.
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1201
Crack detection; Edge detection; Edge grouping; Tensor voting; Shadow removal
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Riding Quality Model for Asphalt Pavement Monitoring Using Phase Array
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IEEE DOI
1109
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1303
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Lin, G.,
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The First-Order Symplectic Euler Method for Simulation of GPR Wave
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1301
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1411
collision avoidance
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1412
asphalt
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Iterative Tensor Voting for Pavement Crack Extraction Using Mobile
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GeoRS(53), No. 3, March 2015, pp. 1527-1537.
IEEE DOI
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crack detection
See also Automated Extraction of Urban Road Facilities Using Mobile Laser Scanning Data.
BibRef
Yi, C.,
Chuang, Y.,
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1508
Feature extraction
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1511
Survey, Pavement Analysis. computer vision
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Measuring Crack Movement in Reinforced Concrete Using Digital Image
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1608
Area measurement
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Idier, J.[Jerome],
Baltazart, V.[Vincent],
Automatic Crack Detection on Two-Dimensional Pavement Images: An
Algorithm Based on Minimal Path Selection,
ITS(17), No. 10, October 2016, pp. 2718-2729.
IEEE DOI
1610
BibRef
Earlier:
A new minimal path selection algorithm for automatic crack detection
on pavement images,
ICIP14(788-792)
IEEE DOI
1502
Context
Cost function
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1610
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IET-ITS(10), No. 9, 2016, pp. 605-612.
DOI Link
1609
asphalt
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Shi, Y.,
Cui, L.,
Qi, Z.,
Meng, F.,
Chen, Z.,
Automatic Road Crack Detection Using Random Structured Forests,
ITS(17), No. 12, December 2016, pp. 3434-3445.
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1612
Feature extraction
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Zaini, N.[Nasrullah],
van der Meer, F.[Freek],
van Ruitenbeek, F.[Frank],
de Smeth, B.[Boudewijn],
Amri, F.[Fadli],
Lievens, C.[Caroline],
An Alternative Quality Control Technique for Mineral Chemistry
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DOI Link
1612
BibRef
Zhang, D.[Dejin],
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Chen, Y.[Ying],
Cao, M.[Min],
He, L.[Li],
Zhang, B.L.[Bai-Ling],
An efficient and reliable coarse-to-fine approach for asphalt
pavement crack detection,
IVC(57), No. 1, 2017, pp. 130-146.
Elsevier DOI
1702
Pavement crack detection
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Casselgren, J.[Johan],
Bodin, U.[Ulf],
Reusable road condition information system for traffic safety and
targeted maintenance,
IET-ITS(11), No. 4, May 2017, pp. 230-238.
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1705
BibRef
Lenglet, C.[Céline],
Blanc, J.[Juliette],
Dubroca, S.[Stéphane],
Smart road that warns its network manager when it begins cracking,
IET-ITS(11), No. 3, April 2017, pp. 152-157.
DOI Link
1705
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González, L.C.,
Moreno, R.,
Escalante, H.J.,
Martínez, F.,
Carlos, M.R.,
Learning Roadway Surface Disruption Patterns Using the Bag of Words
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ITS(18), No. 11, November 2017, pp. 2916-2928.
IEEE DOI
1711
Accelerometers, Automobiles, Data collection, Roads, Sensors,
Smart phones, Urban areas, Roadway surface disruptions,
accelerometer.
BibRef
Carmon, N.[Nimrod],
Ben-Dor, E.[Eyal],
Mapping Asphaltic Roads' Skid Resistance Using Imaging Spectroscopy,
RS(10), No. 3, 2018, pp. xx-yy.
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1804
BibRef
Carlos, M.R.,
Aragón, M.E.,
González, L.C.,
Escalante, H.J.,
Martínez, F.,
Evaluation of Detection Approaches for Road Anomalies Based on
Accelerometer Readings: Addressing Who's Who,
ITS(19), No. 10, October 2018, pp. 3334-3343.
IEEE DOI
1810
Roads, Accelerometers, Sensors, Support vector machines, Proposals,
Acceleration, Accelerometer measurements, mobile sensing,
road anomalies
BibRef
Zhang, Y.,
Chen, C.,
Wu, Q.,
Lu, Q.,
Zhang, S.,
Zhang, G.,
Yang, Y.,
A Kinect-Based Approach for 3D Pavement Surface Reconstruction and
Cracking Recognition,
ITS(19), No. 12, December 2018, pp. 3935-3946.
IEEE DOI
1812
Surface cracks,
Surface reconstruction, Sensors, Image reconstruction,
BibRef
Zhao, S.,
Al-Qadi, I.L.,
Super-Resolution of 3-D GPR Signals to Estimate Thin Asphalt Overlay
Thickness Using the XCMP Method,
GeoRS(57), No. 2, February 2019, pp. 893-901.
IEEE DOI
1901
Signal resolution, Ground penetrating radar, Dielectric constant,
Antennas, Image resolution, Asphalt, Estimation,
thin asphalt overlay
BibRef
Li, Z.Q.[Zhi-Qiang],
Cheng, C.Q.[Cheng-Qi],
Kwan, M.P.[Mei-Po],
Tong, X.C.[Xiao-Chong],
Tian, S.H.[Shao-Hong],
RETRACTION: Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud
Data and Random Forest Classification,
IJGI(8), No. 9, 2019, pp. xx-yy.
DOI Link
1909
BibRef
And:
IJGI(8), No. 1, 2019, pp. xx-yy.
DOI Link
1901
BibRef
Feng, H.[Hui],
Xu, G.S.[Guo-Sheng],
Guo, Y.H.[Yan-Hui],
Multi-scale classification network for road crack detection,
IET-ITS(13), No. 2, February 2019, pp. 398-405.
DOI Link
1902
BibRef
Li, H.,
Song, D.,
Liu, Y.,
Li, B.,
Automatic Pavement Crack Detection by Multi-Scale Image Fusion,
ITS(20), No. 6, June 2019, pp. 2025-2036.
IEEE DOI
1906
Training data, Manuals, Feature extraction, Training, Clutter,
Image edge detection, Fuses, Crack detection,
robotic airport runway inspection
BibRef
Kaddah, W.[Wissam],
Elbouz, M.[Marwa],
Ouerhani, Y.[Yousri],
Baltazart, V.[Vincent],
Alfalou, A.[Ayman],
Optimized minimal path selection (OMPS) method for automatic and
unsupervised crack segmentation within two-dimensional pavement images,
VC(35), No. 9, September 2018, pp. 1293-1309.
Springer DOI
1908
BibRef
Tan, Y.M.[Yu-Min],
Li, Y.X.[Yun-Xin],
UAV Photogrammetry-Based 3D Road Distress Detection,
IJGI(8), No. 9, 2019, pp. xx-yy.
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1909
BibRef
Fan, R.,
Ozgunalp, U.,
Hosking, B.,
Liu, M.,
Pitas, I.,
Pothole Detection Based on Disparity Transformation and Road Surface
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IP(29), No. 1, 2020, pp. 897-908.
IEEE DOI
1910
BibRef
And:
Corrections:
IP(29), 2020, pp. 3091-3091.
IEEE DOI
2002
Pothole detection, road surface modeling.
Roads, Surface treatment,
Surface reconstruction, Detection algorithms, Sea surface,
surface normal
BibRef
Yang, W.W.[Wen-Wei],
Finite element model of concrete material based on CT image
processing technology,
JVCIR(64), 2019, pp. 102631.
Elsevier DOI
1911
CT image, Numerical model, Concrete, Failure process
BibRef
Cheng, L.[Lushan],
Zhang, X.[Xu],
Shen, J.[Jie],
Road surface condition classification using deep learning,
JVCIR(64), 2019, pp. 102638.
Elsevier DOI
1911
Deep learning, Road condition, Activation function,
Image recognition, Intelligent driving
BibRef
Hadavandsiri, Z.[Zahra],
Lichti, D.D.[Derek D.],
Jahraus, A.[Adam],
Jarron, D.[David],
Concrete Preliminary Damage Inspection by Classification of
Terrestrial Laser Scanner Point Clouds through Systematic Threshold
Definition,
IJGI(8), No. 12, 2019, pp. xx-yy.
DOI Link
1912
BibRef
Mohammadi, M.E.[Mohammad Ebrahim],
Wood, R.L.[Richard L.],
Wittich, C.E.[Christine E.],
Non-Temporal Point Cloud Analysis for Surface Damage in Civil
Structures,
IJGI(8), No. 12, 2019, pp. xx-yy.
DOI Link
1912
BibRef
Fei, Y.,
Wang, K.C.P.,
Zhang, A.,
Chen, C.,
Li, J.Q.,
Liu, Y.,
Yang, G.,
Li, B.,
Pixel-Level Cracking Detection on 3D Asphalt Pavement Images Through
Deep-Learning- Based CrackNet-V,
ITS(21), No. 1, January 2020, pp. 273-284.
IEEE DOI
2001
Surface cracks, Asphalt, Libraries,
Feature extraction, Deep learning, Kernel, CrackNet, CrackNet-V,
surface cracks
BibRef
de Blasiis, M.R.[Maria Rosaria],
di Benedetto, A.[Alessandro],
Fiani, M.[Margherita],
Mobile Laser Scanning Data for the Evaluation of Pavement Surface
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RS(12), No. 6, 2020, pp. xx-yy.
DOI Link
2003
BibRef
Kashiyama, T.[Takehiro],
Sekimoto, Y.[Yoshihide],
Seto, T.[Toshikazu],
Lwin, K.K.[Ko Ko],
Analyzing Road Coverage of Public Vehicles According to Number and
Time Period for Installation of Road Inspection Systems,
IJGI(9), No. 3, 2020, pp. xx-yy.
DOI Link
2004
BibRef
Yang, F.,
Zhang, L.,
Yu, S.,
Prokhorov, D.,
Mei, X.,
Ling, H.,
Feature Pyramid and Hierarchical Boosting Network for Pavement Crack
Detection,
ITS(21), No. 4, April 2020, pp. 1525-1535.
IEEE DOI
2004
Feature extraction, Image edge detection, Deep learning, Boosting,
Task analysis, Semantics, Wavelet transforms,
hierarchical boosting
BibRef
Pu, Z.[Ziyuan],
Cui, Z.Y.[Zhi-Yong],
Wang, S.[Shuo],
Li, Q.[Qianmu],
Wang, Y.[Yinhai],
Time-aware gated recurrent unit networks for forecasting road surface
friction using historical data with missing values,
IET-ITS(14), No. 4, April 2020, pp. 213-219.
DOI Link
2004
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Kaddah, W.[Wissam],
Elbouz, M.[Marwa],
Ouerhani, Y.[Yousri],
Alfalou, A.[Ayman],
Desthieux, M.[Marc],
Automatic darkest filament detection (ADFD): a new algorithm for crack
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Springer DOI
2005
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Meyer, F.J.[Franz J.],
Ajadi, O.A.[Olaniyi A.],
Hoppe, E.J.[Edward J.],
Studying the Applicability of X-Band SAR Data to the Network-Scale
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RS(12), No. 9, 2020, pp. xx-yy.
DOI Link
2005
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Xiang, X.Z.[Xue-Zhi],
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IET-IPR(14), No. 8, 19 June 2020, pp. 1580-1586.
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2005
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Zou, L.,
Yi, L.,
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On the Use of Lateral Wave for the Interlayer Debonding Detecting in
an Asphalt Airport Pavement Using a Multistatic GPR System,
GeoRS(58), No. 6, June 2020, pp. 4215-4224.
IEEE DOI
2005
Asphalt airport pavement, common midpoint (CMP),
ground-penetrating radar (GPR), interlayer debonding detection,
nondestructive inspection
BibRef
Du, Y.,
Liu, C.,
Song, Y.,
Li, Y.,
Shen, Y.,
Rapid Estimation of Road Friction for Anti-Skid Autonomous Driving,
ITS(21), No. 6, June 2020, pp. 2461-2470.
IEEE DOI
2006
Roads, Friction, Resistance, Electrical resistance measurement,
Standards, Autonomous vehicles, Immune system, Autonomous vehicle,
velocity control
BibRef
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Reguero, A.M.[Adriana Martínez],
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GPR Spectra for Monitoring Asphalt Pavements,
RS(12), No. 11, 2020, pp. xx-yy.
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2006
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A Nonlocal Laplacian-Based Model for Bituminous Surfacing Crack
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JMIV(62), No. 6-7, July 2020, pp. 1007-1033.
Springer DOI
2007
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Klette, R.,
Pothole Detection Using Computer Vision and Learning,
ITS(21), No. 8, August 2020, pp. 3536-3550.
IEEE DOI
2008
Roads, Image reconstruction, Shape,
Accelerometers, Cameras,
deep learning
BibRef
Abdellatif, M.[Mohamed],
Peel, H.[Harriet],
Cohn, A.G.[Anthony G.],
Fuentes, R.[Raul],
Pavement Crack Detection from Hyperspectral Images Using A Novel
Asphalt Crack Index,
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link
2009
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Fan, R.,
Liu, M.,
Road Damage Detection Based on Unsupervised Disparity Map
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ITS(21), No. 11, November 2020, pp. 4906-4911.
IEEE DOI
2011
Roads, Image segmentation, Cameras, Sensors, numerical solution
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Mattheuwsen, L.[Lukas],
Vergauwen, M.[Maarten],
Manhole Cover Detection on Rasterized Mobile Mapping Point Cloud Data
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DOI Link
2011
BibRef
Özdemir, O.B.[Okan Bilge],
Soydan, H.[Hilal],
Çetin, Y. .Y.[Yasemin Yardimci],
Düzgün, H.S.[Hafize Sebnem],
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2012
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Mettas, C.[Christodoulos],
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Hadjimitsis, D.[Diofantos],
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2012
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Fiorentini, N.[Nicholas],
Maboudi, M.[Mehdi],
Leandri, P.[Pietro],
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Surface Motion Prediction and Mapping for Road Infrastructures
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Yu, Y.[Yang],
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Samali, B.[Bijan],
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Multi-Image-Feature-Based Hierarchical Concrete Crack Identification
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2101
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Zhang, K.,
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IEEE DOI
2102
Training, Feature extraction, Generative adversarial networks,
Image segmentation, Generators, Semantics,
partially accurate ground truths
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Liu, Z.[Zhen],
Wu, W.X.[Wen-Xiu],
Gu, X.Y.[Xing-Yu],
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Application of Combining YOLO Models and 3D GPR Images in Road
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2104
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Pan, J.,
Sun, M.,
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Time-Delay Estimation by a Modified Orthogonal Matching Pursuit
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IEEE DOI
2104
Ground penetrating radar, Matching pursuit algorithms,
Estimation, Frequency measurement, Media, Data models,
time-delay estimation (TDE)
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Dérobert, X.[Xavier],
Baltazart, V.[Vincent],
Simonin, J.M.[Jean-Michel],
Todkar, S.S.[Shreedhar Savant],
Norgeot, C.[Christophe],
Hui, H.Y.[Ho-Yan],
GPR Monitoring of Artificial Debonded Pavement Structures throughout
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2104
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Shen, R.Q.[Rui-Qing],
Zhao, Y.H.[Yong-Hui],
Hu, S.F.[Shu-Fan],
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Reverse-Time Migration Imaging of Ground-Penetrating Radar in NDT of
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DOI Link
2105
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Xu, J.C.[Jun-Cai],
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Recognition of the Typical Distress in Concrete Pavement Based on GPR
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RS(13), No. 12, 2021, pp. xx-yy.
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2106
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Diamanti, N.[Nectaria],
Annan, A.P.[A. Peter],
Jackson, S.R.[Steven R.],
Klazinga, D.[Dylan],
A GPR-Based Pavement Density Profiler: Operating Principles and
Applications,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link
2107
BibRef
Bhattacharya, G.[Gaurab],
Mandal, B.[Bappaditya],
Puhan, N.B.[Niladri B.],
Interleaved Deep Artifacts-Aware Attention Mechanism for Concrete
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IP(30), 2021, pp. 6957-6969.
IEEE DOI
2108
Feature extraction, Computer architecture, Concrete, Aggregates,
Inspection, Monitoring, Meteorology, Fine-grained dense module,
multi-target multi-class classification
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Bhattacharya, G.[Gaurab],
Mandal, B.[Bappaditya],
Puhan, N.B.[Niladri B.],
Multi-Deformation Aware Attention Learning for Concrete Structural
Defect Classification,
CirSysVideo(31), No. 9, September 2021, pp. 3707-3713.
IEEE DOI
2109
Feature extraction, Corrosion, Bars, Tensile stress, Data mining,
Distance measurement, Computer architecture,
multi-target multi-class classification
BibRef
Tankeu, B.T.[Bachir Tchana],
Baltazart, V.[Vincent],
Wang, Y.[Yide],
Guilbert, D.[David],
PUMA Applied to Time Delay Estimation for Processing GPR Data over
Debonded Pavement Structures,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link
2109
BibRef
Rasol, M.[Mezgeen],
Schmidt, F.[Franziska],
Ientile, S.[Silvia],
Adelaide, L.[Lucas],
Nedjar, B.[Boumediene],
Kane, M.[Malal],
Chevalier, C.[Christophe],
Progress and Monitoring Opportunities of Skid Resistance in Road
Transport: A Critical Review and Road Sensors,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link
2109
BibRef
Song, Y.Z.[Yong-Ze],
Wu, P.[Peng],
Gilmore, D.[Daniel],
Li, Q.[Qindong],
A Spatial Heterogeneity-Based Segmentation Model for Analyzing Road
Deterioration Network Data in Multi-Scale Infrastructure Systems,
ITS(22), No. 11, November 2021, pp. 7073-7083.
IEEE DOI
2112
Roads, Monitoring, Australia, Data models, Data analysis,
Spatial databases, Image segmentation,
spatial analysis
BibRef
Fan, R.[Rui],
Wang, H.[Hengli],
Wang, Y.[Yuan],
Liu, M.[Ming],
Pitas, I.[Ioannis],
Graph Attention Layer Evolves Semantic Segmentation for Road Pothole
Detection: A Benchmark and Algorithms,
IP(30), 2021, pp. 8144-8154.
IEEE DOI
2110
Roads, Image segmentation, Semantics,
Convolutional neural networks, Feature extraction,
graph neural network
BibRef
Cao, Q.Q.[Qing-Qing],
Al-Qadi, I.L.[Imad L.],
Effect of Moisture Content on Calculated Dielectric Properties of
Asphalt Concrete Pavements from Ground-Penetrating Radar Measurements,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link
2201
BibRef
Chen, C.[Cheng],
Chandra, S.[Sindhu],
Han, Y.F.[Yu-Fan],
Seo, H.[Hyungjoon],
Deep Learning-Based Thermal Image Analysis for Pavement Defect
Detection and Classification Considering Complex Pavement Conditions,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link
2201
BibRef
Wei, Z.X.[Zi-Xian],
SUN, T.[Tao],
Wu, Y.[Yuhao],
Zhou, L.Q.[Li-Qing],
Ruan, X.L.[Xiao-Li],
Pavement crack detection using non-local theory and iterative
sampling,
IET-IPR(16), No. 3, 2022, pp. 869-877.
DOI Link
2202
BibRef
Fan, L.[Lili],
Zhao, H.W.[Hong-Wei],
Li, Y.[Ying],
Li, S.[Shen],
Zhou, R.[Rui],
Chu, W.[Wenbo],
RAO-UNet: a residual attention and octave UNet for road crack
detection via balance loss,
IET-ITS(16), No. 3, 2022, pp. 332-343.
DOI Link
2202
BibRef
Sun, M.[Mingsi],
Zhao, H.W.[Hong-Wei],
Li, J.[Jiao],
Road crack detection network under noise based on feature pyramid
structure with feature enhancement (road crack detection under noise),
IET-IPR(16), No. 3, 2022, pp. 809-822.
DOI Link
2202
BibRef
Simonin, J.M.[Jean-Michel],
Piau, J.M.[Jean-Michel],
Le-Boursicault, V.[Vinciane],
Freitas, M.[Murilo],
Orthogonal Set of Indicators for the Assessment of Flexible Pavement
Stiffness from Deflection Monitoring: Theoretical Formalism and
Numerical Study,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Guo, S.[Shili],
Xu, Z.W.[Zhi-Wei],
Li, X.[Xiuzhong],
Zhu, P.[Peimin],
Detection and Characterization of Cracks in Highway Pavement with the
Amplitude Variation of GPR Diffracted Waves: Insights from Forward
Modeling and Field Data,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Zhang, L.L.[Liang-Liang],
Wang, L.[Lin],
Yang, B.[Bo],
Niu, S.[Sijie],
Han, Y.M.[Ya-Min],
Oh, S.K.[Sung-Kwun],
Rapid construction of 4D high-quality microstructural image for
cement hydration using partial information registration,
PR(124), 2022, pp. 108471.
Elsevier DOI
2203
Cement hydration, Rapid image construction, Image registration,
Particle swarm optimization, Microstructural temporal image sequences
BibRef
Zhou, Q.[Qiang],
Qu, Z.[Zhong],
Ju, F.R.[Fang-Rong],
A multi-scale learning method with dilated convolutional network for
concrete surface cracks detection,
IET-IPR(16), No. 5, 2022, pp. 1389-1402.
DOI Link
2203
BibRef
Yu, Y.T.[Yong-Tao],
Guan, H.Y.[Hai-Yan],
Li, D.[Dilong],
Zhang, Y.J.[Yong-Jun],
Jin, S.H.[Sheng-Hua],
Yu, C.H.[Chang-Hui],
CCapFPN: A Context-Augmented Capsule Feature Pyramid Network for
Pavement Crack Detection,
ITS(23), No. 4, April 2022, pp. 3324-3335.
IEEE DOI
2204
Feature extraction, Deep learning, Image edge detection,
Task analysis, Monitoring, Neurons, Fuses, Crack detection, deep learning
BibRef
Chen, Y.[Yihan],
Gu, X.Y.[Xing-Yu],
Liu, Z.[Zhen],
Liang, J.[Jia],
A Fast Inference Vision Transformer for Automatic Pavement Image
Classification and Its Visual Interpretation Method,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Ling, J.Y.[Jian-Yu],
Qian, R.[Rongyi],
Shang, K.[Ke],
Guo, L.[Linyan],
Zhao, Y.[Yu],
Liu, D.[Dongyi],
Research on the Dynamic Monitoring Technology of Road Subgrades with
Time-Lapse Full-Coverage 3D Ground Penetrating Radar (GPR),
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Daraghmi, Y.A.[Yousef-Awwad],
Wu, T.H.[Tsung-Hsiang],
Ik, T.U.[Tsě-Uí],
Crowdsourcing-Based Road Surface Evaluation and Indexing,
ITS(23), No. 5, May 2022, pp. 4164-4175.
IEEE DOI
2205
Roads, Rough surfaces, Surface roughness, Monitoring, Smart phones,
Vibrations, Standards, Crowdsourcing, road roughness,
power spectral density
BibRef
Ahmed, A.[Adeel],
Ashfaque, M.[Moeez],
Ulhaq, M.U.[Muhammad Uzair],
Mathavan, S.[Senthan],
Kamal, K.[Khurram],
Rahman, M.[Mujib],
Pothole 3D Reconstruction With a Novel Imaging System and Structure
From Motion Techniques,
ITS(23), No. 5, May 2022, pp. 4685-4694.
IEEE DOI
2205
Cameras, Roads, Image reconstruction,
Surface reconstruction, Measurement by laser beam,
metrology
BibRef
Chen, Z.P.[Zhi-Peng],
Li, Q.Q.[Qing-Quan],
Xue, W.X.[Wei-Xin],
Zhang, D.[Dejin],
Xiong, S.[Siting],
Yin, Y.[Yu],
Lv, S.[Shiwang],
Rapid Inspection of Large Concrete Floor Flatness Using Wheeled Robot
with Aided-INS,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Yuan, G.[Genji],
Li, J.B.[Jian-Bo],
Meng, X.L.[Xiang-Long],
Li, Y.[Yinong],
CurSeg: A pavement crack detector based on a deep hierarchical
feature learning segmentation framework,
IET-ITS(16), No. 6, 2022, pp. 782-799.
DOI Link
2205
BibRef
Nayyeri, F.[Fereshteh],
Zhou, J.[Jun],
Multi-Resolution ResNet for Road and Bridge Crack Detection,
DICTA21(1-8)
IEEE DOI
2201
Training, Bridges, Image resolution, Computational modeling, Roads,
Image edge detection, Digital images, crack detection, ResNet,
crack dataset
BibRef
Shahbazi, L.[Leila],
Majidi, B.[Babak],
Movaghar, A.[Ali],
Autonomous Road Pavement Inspection and Defect Analysis for Smart
City Maintenance,
IPRIA21(1-5)
IEEE DOI
2201
Deep learning, Visualization, Asphalt, Smart cities, Roads, Inspection,
Safety, Road pavement crack detection,
smart city
BibRef
Duan, L.[Lijuan],
Zeng, J.[Jun],
Pang, J.[Junbiao],
Wang, J.Z.[Jun-Zhe],
Pavement Crack Detection Using Multi-stage Structural Feature
Extraction Model,
ICIP21(969-973)
IEEE DOI
2201
Training, Art, Roads, Maintenance engineering, Feature extraction,
Complexity theory, crack detection, deep supervision,
structural feature extraction
BibRef
Zhang, Y.[Yujia],
Li, Q.[Qianzhong],
Zhao, X.G.[Xiao-Guang],
Tan, M.[Min],
TB-Net: A Three-Stream Boundary-Aware Network for Fine-Grained
Pavement Disease Segmentation,
WACV21(3654-3663)
IEEE DOI
2106
Convolution, Roads, Inspection, Maintenance engineering, Safety
BibRef
Masihullah, S.[Shaik],
Garg, R.[Ritu],
Mukherjee, P.[Prerana],
Ray, A.[Anupama],
Attention Based Coupled Framework for Road and Pothole Segmentation,
ICPR21(5812-5819)
IEEE DOI
2105
Training, Image segmentation, Visualization, Rain, Roads, Snow,
Vehicle safety, Pothole Detection, Road Segmentation,
Deep Networks
BibRef
Wu, X.Y.[Xuan-Yi],
Ma, J.F.[Jian-Fei],
Sun, Y.[Yu],
Zhao, C.[Chenqiu],
Basu, A.[Anup],
Multi-Scale Deep Pixel Distribution Learning for Concrete Crack
Detection,
ICPR21(6577-6583)
IEEE DOI
2105
Deep learning, Learning systems, Image segmentation,
Feature extraction, Pattern recognition, Surface cracks,
concrete crack detection
BibRef
Inoue, Y.[Yuki],
Nagayoshi, H.[Hiroto],
Crack Detection as a Weakly-Supervised Problem: Towards Achieving
Less Annotation-Intensive Crack Detectors,
ICPR21(65-72)
IEEE DOI
2105
Annotations, Roads, Semantics, Buildings, Brightness, Detectors, Inspection
BibRef
Chitale, P.A.,
Kekre, K.Y.,
Shenai, H.R.,
Karani, R.,
Gala, J.P.,
Pothole Detection and Dimension Estimation System using Deep Learning
(YOLO) and Image Processing,
IVCNZ20(1-6)
IEEE DOI
2012
Deep learning, Pandemics, Shape, Roads, Image processing, Estimation,
Maintenance engineering, YOLO, Deep Learning,
Dimension Estimation
BibRef
Cannelle, B.,
Beltzung, F.,
Thiémard-Spada, M.,
Application of Photogrammetry and Image Processing for the Study Of
Porous Surface Courses,
ISPRS20(B2:745-749).
DOI Link
2012
BibRef
Ravi, R.,
Bullock, D.,
Habib, A.,
Highway and Airport Runway Pavement Inspection Using Mobile Lidar,
ISPRS20(B1:349-354).
DOI Link
2012
BibRef
Pontoglio, E.,
Colucci, E.,
Lingua, A.,
Maschio, P.,
Migliazza, M.R.,
Scavia, C.,
UAV and Close-range Photogrammetry to Support Geo-mechanical Analysis
In Safety Road Management: the 'Vallone d'Elva' Road,
ISPRS20(B2:1159-1166).
DOI Link
2012
BibRef
Pinto, L.,
Bianchini, F.,
Nova, V.,
Passoni, D.,
Low-cost UAS Photogrammetry for Road Infrastructures' Inspection,
ISPRS20(B2:1145-1150).
DOI Link
2012
BibRef
Ono, Y.,
Tsuji, A.,
Abe, J.,
Noguchi, H.,
Abe, J.,
Robust Detection of Surface Anomaly Using Lidar Point Cloud With
Intensity,
ISPRS20(B2:1129-1136).
DOI Link
2012
BibRef
Shokri, P.,
Shahbazi, M.,
Lichti, D.,
Nielsen, J.,
Vision-based Approaches for Quantifying Cracks In Concrete Structures,
ISPRS20(B2:1167-1174).
DOI Link
2012
BibRef
Niu, B.,
Wu, H.,
Meng, Y.,
Application of CEM Algorithm in the Field of Tunnel Crack
Identification,
ICIVC20(232-236)
IEEE DOI
2009
Feature extraction, Gabor filters, Road transportation,
Filtering algorithms, Image edge detection, Inspection,
CEM
BibRef
Benz, C.,
Debus, P.,
Ha, H.K.,
Rodehorst, V.,
Crack Segmentation on UAS-based Imagery using Transfer Learning,
IVCNZ19(1-6)
IEEE DOI
2004
Code, Crack Detection.
WWW Link. autonomous aerial vehicles, convolutional neural nets,
crack detection, image resolution, image segmentation,
UAS
BibRef
Park, J.S.,
Lee, K.S.,
Kim, S.,
Assessment for a Condition Using Terrestrial Lidar Data,
Gi4DM19(311-314).
DOI Link
1912
Potholes, etc.
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d'Aranno, P.,
di Benedetto, A.,
Fiani, M.,
Marsella, M.,
Remote Sensing Technologies for Linear Infrastructure Monitoring,
GEORES19(461-468).
DOI Link
1912
E.g. roads.
BibRef
Liebold, F.,
Maas, H.G.,
Heravi, A.A.,
Crack Width Measurement for Non-planar Surfaces By Triangle Mesh
Analysis in Civil Engineering Material Testing,
Optical3D19(107-113).
DOI Link
1912
BibRef
Seydi, S.T.,
Rastiveis, H.,
A Deep Learning Framework for Roads Network Damage Assessment Using
Post-earthquake Lidar Data,
SMPR19(955-961).
DOI Link
1912
BibRef
Fakhri, S.A.,
Fakhri, S.A.,
Saadatseresht, M.,
Road Crack Detection Using Gaussian/prewitt Filter,
SMPR19(371-377).
DOI Link
1912
BibRef
Truong-Hong, L.,
Laefer, D.F.,
Lindenbergh, R.C.,
Automatic Detection of Road Edges From Aerial Laser Scanning Data,
Laser19(1135-1140).
DOI Link
1912
BibRef
van der Horst, B.B.,
Lindenbergh, R.C.,
Puister, S.W.J.,
Mobile Laser Scan Data for Road Surface Damage Detection,
Laser19(1141-1148).
DOI Link
1912
BibRef
König, J.,
Jenkins, M.D.[M. David],
Barrie, P.,
Mannion, M.,
Morison, G.,
A Convolutional Neural Network for Pavement Surface Crack
Segmentation Using Residual Connections and Attention Gating,
ICIP19(1460-1464)
IEEE DOI
1910
Semantic Segmentation, Attention, Residual Connections, U-Net, Surface Cracks
BibRef
Dhiman, A.,
Chien, H.,
Klette, R.,
Road surface distress detection in disparity space,
IVCNZ17(1-6)
IEEE DOI
1902
road accidents, road traffic, roads, stereo image processing,
road surface distress detection, traffic accidents,
Sensors
BibRef
Yang, L.,
Li, B.,
Li, W.,
Jiang, B.,
Xiao, J.,
Semantic Metric 3D Reconstruction for Concrete Inspection,
Odometry18(1624-16248)
IEEE DOI
1812
Inspection, Semantics, Measurement,
Visualization, Simultaneous localization and mapping, Image segmentation
BibRef
Song, W.,
Workman, S.,
Hadzic, A.,
Zhang, X.,
Green, E.,
Chen, M.,
Souleyrette, R.,
Jacobs, N.,
FARSA: Fully Automated Roadway Safety Assessment,
WACV18(521-529)
IEEE DOI
1806
image processing, neural nets, road safety, roads,
traffic engineering computing, FARSA, US Road Assessment Program,
Training
BibRef
Liu, X.Z.[Xiang-Zeng],
Ai, Y.F.[Yun-Feng],
Scherer, S.[Sebastian],
Robust image-based crack detection in concrete structure using
multi-scale enhancement and visual features,
ICIP17(2304-2308)
IEEE DOI
1803
Indexes, Crack detection, concrete structure, guided filter, image enhancement
BibRef
Güldür Erkal, B.,
Apaydin, N.M.,
Bridge Surface Damage Detection Application with A Laser-based Software
Prototype,
GeoAdvances17(55-57).
DOI Link
1805
BibRef
Grünauer, A.[Andreas],
Halmetschlager-Funek, G.[Georg],
Prankl, J.[Johann],
Vincze, M.[Markus],
Learning the Floor Type for Automated Detection of Dirt Spots for
Robotic Floor Cleaning Using Gaussian Mixture Models,
CVS17(576-589).
Springer DOI
1711
BibRef
Chaudhury, S.,
Nakano, G.,
Takada, J.,
Iketani, A.,
Spatial-Temporal Motion Field Analysis for Pixelwise Crack Detection
on Concrete Surfaces,
WACV17(336-344)
IEEE DOI
1609
Bridges, Concrete, Labeling, Loading, Maintenance engineering, Safety,
Surface cracks.
BibRef
Martínez-Sánchez, J.,
Puente, I.,
GonzálezJorge, H.,
Riveiro, B.,
Arias, P.,
Automatic Thickness And Volume Estimation Of Sprayed Concrete On
Anchored Retaining Walls From Terrestrial Lidar Data,
ISPRS16(B5: 521-526).
DOI Link
1610
BibRef
Abdic, I.,
Fridman, L.,
Brown, D.E.,
Angell, W.,
Reimer, B.,
Marchi, E.,
Schuller, B.,
Detecting road surface wetness from audio: A deep learning approach,
ICPR16(3458-3463)
IEEE DOI
1705
Cameras, Data collection, Recurrent neural networks, Roads,
Rough surfaces, Spectrogram, Tires
BibRef
Vandoni, J.,
Le Hégarat-Mascle, S.,
Aldea, E.,
Crack detection based on a Marked Point Process model,
ICPR16(3933-3938)
IEEE DOI
1705
Adaptation models, Data models, Extremities, Image segmentation,
Joining processes, Roads, Robustness
BibRef
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