Yamaguchi, T.[Tomoyuki],
Hashimoto, S.[Shuji],
Fast crack detection method for large-size concrete surface images
using percolation-based image processing,
MVA(21), No. 5, August 2010, pp. 797-809.
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1011
BibRef
Earlier:
Improved percolation-based method for crack detection in concrete
surface images,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Fujita, Y.[Yusuke],
Hamamoto, Y.[Yoshihiko],
A robust automatic crack detection method from noisy concrete surfaces,
MVA(22), No. 2, March 2011, pp. 245-254.
WWW Link.
1103
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Li, Q.Q.[Qing-Quan],
Zou, Q.[Qin],
Zhang, D.Q.[Da-Qiang],
Mao, Q.Z.[Qing-Zhou],
FoSA: F* Seed-growing Approach for crack-line detection from pavement
images,
IVC(29), No. 12, November 2011, pp. 861-872.
Elsevier DOI
1112
Line detection; Pavement crack; Seed-growing; Dynamic programming
BibRef
Zou, Q.[Qin],
Cao, Y.[Yu],
Li, Q.Q.[Qing-Quan],
Mao, Q.Z.[Qing-Zhou],
Wang, S.[Song],
CrackTree: Automatic crack detection from pavement images,
PRL(33), No. 3, 1 February 2012, pp. 227-238.
Elsevier DOI
1201
Crack detection; Edge detection; Edge grouping; Tensor voting; Shadow removal
BibRef
Oliveira, H.,
Correia, P.L.,
Automatic Road Crack Detection and Characterization,
ITS(14), No. 1, March 2013, pp. 155-168.
IEEE DOI
1303
BibRef
Guan, H.[Haiyan],
Li, J.,
Yu, Y.T.[Yong-Tao],
Chapman, M.,
Wang, H.Y.[Han-Yun],
Wang, C.[Cheng],
Zhai, R.F.[Rui-Fang],
Iterative Tensor Voting for Pavement Crack Extraction Using Mobile
Laser Scanning Data,
GeoRS(53), No. 3, March 2015, pp. 1527-1537.
IEEE DOI
1412
crack detection
See also Automated Extraction of Urban Road Facilities Using Mobile Laser Scanning Data.
BibRef
Hoult, N.A.,
Dutton, M.,
Hoag, A.,
Take, W.A.,
Measuring Crack Movement in Reinforced Concrete Using Digital Image
Correlation: Overview and Application to Shear Slip Measurements,
PIEEE(104), No. 8, August 2016, pp. 1561-1574.
IEEE DOI
1608
Area measurement
BibRef
Amhaz, R.[Rabih],
Chambon, S.[Sylvie],
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
BibRef
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.
IEEE DOI
1612
Feature extraction
BibRef
Zhang, D.[Dejin],
Li, Q.Q.[Qing-Quan],
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
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
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
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
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
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
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
extraction on two-dimensional pavement images,
VC(36), No. 7, July 2020, pp. 1369-1384.
Springer DOI
2005
BibRef
Xiang, X.Z.[Xue-Zhi],
Zhang, Y.Q.[Yu-Qi],
El Saddik, A.[Abdulmotaleb],
Pavement crack detection network based on pyramid structure and
attention mechanism,
IET-IPR(14), No. 8, 19 June 2020, pp. 1580-1586.
DOI Link
2005
BibRef
Debroux, N.[Noémie],
Le Guyader, C.[Carole],
Vese, L.A.[Luminita A.],
A Nonlocal Laplacian-Based Model for Bituminous Surfacing Crack
Recovery and its MPI Implementation,
JMIV(62), No. 6-7, July 2020, pp. 1007-1033.
Springer DOI
2007
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
BibRef
Yu, Y.[Yang],
Rashidi, M.[Maria],
Samali, B.[Bijan],
Yousefi, A.M.[Amir M.],
Wang, W.Q.[Wei-Qiang],
Multi-Image-Feature-Based Hierarchical Concrete Crack Identification
Framework Using Optimized SVM Multi-Classifiers and D-S Fusion
Algorithm for Bridge Structures,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link
2101
BibRef
Zhang, K.,
Zhang, Y.,
Cheng, H.D.,
CrackGAN: Pavement Crack Detection Using Partially Accurate Ground
Truths Based on Generative Adversarial Learning,
ITS(22), No. 2, February 2021, pp. 1306-1319.
IEEE DOI
2102
Training, Feature extraction, Generative adversarial networks,
Image segmentation, Generators, Semantics,
partially accurate ground truths
BibRef
Wei, Z.X.[Zi-Xian],
SUN, T.[Tao],
Wu, Y.H.[Yu-Hao],
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.B.[Wen-Bo],
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
Guo, S.[Shili],
Xu, Z.W.[Zhi-Wei],
Li, X.Z.[Xiu-Zhong],
Zhu, P.M.[Pei-Min],
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
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
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
Park, M.J.[Min Jae],
Kim, J.[Jihyung],
Jeong, S.[Sanggi],
Jang, A.[Arum],
Bae, J.[Jaehoon],
Ju, Y.K.[Young K.],
Machine Learning-Based Concrete Crack Depth Prediction Using Thermal
Images Taken under Daylight Conditions,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Schladitz, K.[Katja],
Methods for segmenting cracks in 3d images of concrete:
A comparison based on semi-synthetic images,
PR(129), 2022, pp. 108747.
Elsevier DOI
2206
Computed tomography, Fractional Brownian surface,
3d segmentation, Crack detection, Machine learning, Deep learning
BibRef
Ji, K.[Kun],
Zhang, Z.H.[Zhen-Hai],
Yu, J.L.[Jia-Le],
Dang, J.W.[Jian-Wu],
A deep learning-based method for pixel-level crack detection on
concrete bridges,
IET-IPR(16), No. 10, 2022, pp. 2609-2622.
DOI Link
2207
BibRef
Zhou, Q.[Qiang],
Qu, Z.[Zhong],
Cao, C.[Chong],
Mixed pooling and richer attention feature fusion for crack detection,
PRL(145), 2021, pp. 96-102.
Elsevier DOI
2104
Crack detection, Mixed pooling, Spatial attention, Channel-wise attention
BibRef
Qu, Z.[Zhong],
Chen, W.[Wen],
Wang, S.Y.[Shi-Yan],
Yi, T.M.[Tu-Ming],
Liu, L.[Ling],
A Crack Detection Algorithm for Concrete Pavement Based on Attention
Mechanism and Multi-Features Fusion,
ITS(23), No. 8, August 2022, pp. 11710-11719.
IEEE DOI
2208
Feature extraction, Semantics, Decoding, Encoding,
Intelligent transportation systems, Detection algorithms,
multi-features fusion
BibRef
Zhang, Y.[Yi],
Fan, J.F.[Jun-Fu],
Zhang, M.Z.[Meng-Zhen],
Shi, Z.W.[Zong-Wen],
Liu, R.F.[Ru-Fei],
Guo, B.[Bing],
A Recurrent Adaptive Network: Balanced Learning for Road Crack
Segmentation with High-Resolution Images,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Feng, H.F.[Hui-Fang],
Li, W.[Wen],
Luo, Z.P.[Zhi-Peng],
Chen, Y.P.[Yi-Ping],
Fatholahi, S.N.[Sarah Narges],
Cheng, M.[Ming],
Wang, C.[Cheng],
Junior, J.M.[José Marcato],
Li, J.[Jonathan],
GCN-Based Pavement Crack Detection Using Mobile LiDAR Point Clouds,
ITS(23), No. 8, August 2022, pp. 11052-11061.
IEEE DOI
2208
Roads, Feature extraction, Learning systems, Laser radar, Inspection,
Shape, Pavement crack detection, MLS point clouds, semi-supervised, GCN
BibRef
Feng, H.F.[Hui-Fang],
Li, W.[Wen],
Ma, L.F.[Ling-Fei],
Chen, Y.P.[Yi-Ping],
Guan, H.Y.[Hai-Yan],
Yu, Y.T.[Yong-Tao],
Junior, J.M.[José Marcato],
Li, J.[Jonathan],
Crack-U2Net: Multiscale Feature Learning Network for Pavement Crack
Detection from Large-Scale MLS Point Clouds,
ITS(25), No. 11, November 2024, pp. 17952-17964.
IEEE DOI
2411
Feature extraction, Point cloud compression, Accuracy, Roads,
Training data, Deep learning, Representation learning, data augmentation
BibRef
Zhu, W.X.[Wen-Xuan],
Tan, W.K.[Wei-Kan],
Ma, L.F.[Ling-Fei],
Zhang, D.D.[De-Dong],
Li, J.[Jonathan],
Chapman, M.A.[Michael A.],
A Capsnets Approach to Pavement Crack Detection Using Mobile Laser
Scannning Point Clouds,
ISPRS21(B1-2021: 39-44).
DOI Link
2201
BibRef
Qu, Z.[Zhong],
Wang, C.Y.[Cai-Yun],
Wang, S.Y.[Shi-Yan],
Ju, F.R.[Fang-Rong],
A Method of Hierarchical Feature Fusion and Connected Attention
Architecture for Pavement Crack Detection,
ITS(23), No. 9, September 2022, pp. 16038-16047.
IEEE DOI
2209
Convolution, Feature extraction, Kernel, Roads, Deep learning,
Image edge detection, dilated convolution
BibRef
Zhang, Y.J.[Yu-Jia],
Wu, J.X.[Jun-Xian],
Li, Q.Z.[Qian-Zhong],
Zhao, X.G.[Xiao-Guang],
Tan, M.[Min],
Beyond Crack: Fine-Grained Pavement Defect Segmentation Using
Three-Stream Neural Networks,
ITS(23), No. 9, September 2022, pp. 14820-14832.
IEEE DOI
2209
Image segmentation, Task analysis, Shape, Inspection, Roads, Lighting,
Maintenance engineering, Fine-grained defect segmentation,
pavement inspection
BibRef
Liao, J.H.[Jiang-Hai],
Yue, Y.H.[Yuan-Hao],
Zhang, D.[Dejin],
Tu, W.[Wei],
Cao, R.[Rui],
Zou, Q.[Qin],
Li, Q.Q.[Qing-Quan],
Automatic Tunnel Crack Inspection Using an Efficient Mobile Imaging
Module and a Lightweight CNN,
ITS(23), No. 9, September 2022, pp. 15190-15203.
IEEE DOI
2209
Inspection, Cameras, Imaging, Charge coupled devices, Sensors,
Feature extraction, Surface emitting lasers, Tunnel inspection,
spatial constraint
BibRef
Liu, C.Q.[Chuan-Qi],
Zhu, C.G.[Cheng-Guang],
Xia, X.[Xuan],
Zhao, J.K.[Jian-Kang],
Long, H.H.[Hai-Hui],
FFEDN: Feature Fusion Encoder Decoder Network for Crack Detection,
ITS(23), No. 9, September 2022, pp. 15546-15557.
IEEE DOI
2209
Feature extraction, Decoding, Shape, Semantics, Interference,
Task analysis, Image edge detection, Crack detection,
shape semantic prior
BibRef
Sun, X.Z.[Xin-Zi],
Xie, Y.C.[Yuan-Chang],
Jiang, L.M.[Li-Ming],
Cao, Y.[Yu],
Liu, B.[Benyuan],
DMA-Net: DeepLab With Multi-Scale Attention for Pavement Crack
Segmentation,
ITS(23), No. 10, October 2022, pp. 18392-18403.
IEEE DOI
2210
Image segmentation, Convolution, Semantics, Roads, Decoding,
Feature extraction, Pavement crack segmentation,
multi-scale attention
BibRef
Zhou, Q.[Qiang],
Qu, Z.[Zhong],
Wang, S.Y.[Shi-Yan],
Bao, K.H.[Kang-Hua],
A Method of Potentially Promising Network for Crack Detection With
Enhanced Convolution and Dynamic Feature Fusion,
ITS(23), No. 10, October 2022, pp. 18736-18745.
IEEE DOI
2210
Convolution, Feature extraction, Strips, Kernel, Task analysis,
Deep learning, Surface treatment, Crack detection,
dynamic feature fusion
BibRef
Fang, J.[Jie],
Yang, C.[Chen],
Shi, Y.[Yuetian],
Wang, N.[Nan],
Zhao, Y.[Yang],
External Attention Based TransUNet and Label Expansion Strategy for
Crack Detection,
ITS(23), No. 10, October 2022, pp. 19054-19063.
IEEE DOI
2210
Feature extraction, Transformers, Roads, Mathematical models,
Deep learning, Convolution, Semantics, Crack detection, TransUNet,
label expansion
BibRef
Sekar, A.[Aravindkumar],
Perumal, V.[Varalakshmi],
CFC-GAN: Forecasting Road Surface Crack Using Forecasted Crack
Generative Adversarial Network,
ITS(23), No. 11, November 2022, pp. 21378-21391.
IEEE DOI
2212
Roads, Generative adversarial networks, Predictive models, Faces,
Surface cracks, Forecasting, Aging, road crack forecasting
BibRef
Han, C.J.[Cheng-Jia],
Ma, T.[Tao],
Huyan, J.[Ju],
Huang, X.M.[Xiao-Ming],
Zhang, Y.N.[Yan-Ning],
CrackW-Net: A Novel Pavement Crack Image Segmentation Convolutional
Neural Network,
ITS(23), No. 11, November 2022, pp. 22135-22144.
IEEE DOI
2212
Image segmentation, Convolution, Convolutional neural networks,
Roads, Feature extraction, Task analysis, Neural networks,
semantic segmentation
BibRef
Liu, F.Y.[Fang-Yu],
Liu, J.[Jian],
Wang, L.[Linbing],
Asphalt Pavement Crack Detection Based on Convolutional Neural
Network and Infrared Thermography,
ITS(23), No. 11, November 2022, pp. 22145-22155.
IEEE DOI
2212
Computational modeling, Image segmentation, Complexity theory,
Cameras, Measurement, Convolutional neural networks, Asphalt,
asphalt pavement
BibRef
Hou, Y.[Yue],
Liu, S.[Shuo],
Cao, D.D.[Dan-Dan],
Peng, B.[Bo],
Liu, Z.[Zhuo],
Sun, W.J.[Wen-Juan],
Chen, N.[Ning],
A Deep Learning Method for Pavement Crack Identification Based on
Limited Field Images,
ITS(23), No. 11, November 2022, pp. 22156-22165.
IEEE DOI
2212
Convolutional neural networks, Generative adversarial networks,
Image processing, Data models, Training, Image edge detection, pavement
BibRef
Ma, D.[Duo],
Fang, H.Y.[Hong-Yuan],
Wang, N.[Niannian],
Zhang, C.[Chao],
Dong, J.X.[Jia-Xiu],
Hu, H.[Haobang],
Automatic Detection and Counting System for Pavement Cracks Based on
PCGAN and YOLO-MF,
ITS(23), No. 11, November 2022, pp. 22166-22178.
IEEE DOI
2212
Autonomous aerial vehicles, Graphics processing units,
Generative adversarial networks, Roads, Real-time systems,
crack tracking and counting
BibRef
Yao, H.[Hui],
Liu, Y.[Yanhao],
Li, X.[Xin],
You, Z.[Zhanping],
Feng, Y.[Yu],
Lu, W.W.[Wei-Wei],
A Detection Method for Pavement Cracks Combining Object Detection and
Attention Mechanism,
ITS(23), No. 11, November 2022, pp. 22179-22189.
IEEE DOI
2212
Feature extraction, Object detection, Neural networks, Roads,
Computational modeling, Image segmentation,
attention mechanism
BibRef
Liu, Z.[Zhen],
Gu, X.Y.[Xing-Yu],
Yang, H.[Hailu],
Wang, L.[Lutai],
Chen, Y.[Yihan],
Wang, D.Y.[Dan-Yu],
Novel YOLOv3 Model With Structure and Hyperparameter Optimization for
Detection of Pavement Concealed Cracks in GPR Images,
ITS(23), No. 11, November 2022, pp. 22258-22268.
IEEE DOI
2212
Roads, Feature extraction, Training, Optimization, Bayes methods,
Antenna arrays, Ground penetrating radar, concealed cracks,
Bayesian optimization
BibRef
del Río-Barral, P.[Pablo],
Soilán, M.[Mario],
González-Collazo, S.M.[Silvia María],
Arias, P.[Pedro],
Pavement Crack Detection and Clustering via Region-Growing Algorithm
from 3D MLS Point Clouds,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Shokri, P.[Parnia],
Shahbazi, M.[Mozhdeh],
Nielsen, J.[John],
Semantic Segmentation and 3D Reconstruction of Concrete Cracks,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Hu, Q.F.[Qing-Feng],
Wang, P.[Peng],
Li, S.M.[Shi-Ming],
Liu, W.K.[Wen-Kai],
Li, Y.F.[Yi-Fan],
Lu, W.Q.[Wei-Qiang],
Kou, Y.C.[Ying-Chao],
Wei, F.P.[Fu-Peng],
He, P.P.[Pei-Pei],
Yu, A.[Anzhu],
Research on Intelligent Crack Detection in a Deep-Cut Canal Slope in
the Chinese South-North Water Transfer Project,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Guo, J.M.[Jing-Ming],
Markoni, H.[Herleeyandi],
Efficient and Adaptable Patch-Based Crack Detection,
ITS(23), No. 11, November 2022, pp. 21885-21896.
IEEE DOI
2212
Decoding, Feature extraction, Transformers, Roads, Detectors,
Convolution, Inspection, Patch-based processing, CNN, Linformer,
demanded controller
BibRef
König, J.[Jacob],
Jenkins, M.D.[Mark David],
Mannion, M.[Mike],
Barrie, P.[Peter],
Morison, G.[Gordon],
Weakly-Supervised Surface Crack Segmentation by Generating
Pseudo-Labels Using Localization With a Classifier and Thresholding,
ITS(23), No. 12, December 2022, pp. 24083-24094.
IEEE DOI
2212
Image segmentation, Surface cracks, Location awareness, Training,
Surface morphology, Standards, Convolutional neural networks,
neural networks
BibRef
Tian, Y.L.[Yao-Lin],
Wan, X.[Xue],
Wu, A.[Aodi],
Zhao, G.Y.[Guang-Yuan],
Scene Aware Semantic Crack Segmentation from Oblique Drone Imagery,
ICPR22(585-592)
IEEE DOI
2212
Degradation, Image segmentation, Roads, Semantics, Buildings,
Feature extraction, Computational efficiency
BibRef
Li, K.[Kai],
Wang, B.[Bo],
Tian, Y.J.[Ying-Jie],
Qi, Z.Q.[Zhi-Quan],
Fast and Accurate Road Crack Detection Based on Adaptive
Cost-Sensitive Loss Function,
Cyber(53), No. 2, February 2023, pp. 1051-1062.
IEEE DOI
2301
Roads, Training, Costs, Image edge detection, Training data,
Sampling methods, Adaptation models, Crack detection,
weighted cross-entropy (WCE)
BibRef
Liu, H.J.[Hui-Jun],
Yang, C.H.[Chun-Hua],
Li, A.[Ao],
Huang, S.[Sheng],
Feng, X.[Xin],
Ruan, Z.M.[Zhi-Min],
Ge, Y.X.[Yong-Xin],
Deep Domain Adaptation for Pavement Crack Detection,
ITS(24), No. 2, February 2023, pp. 1669-1681.
IEEE DOI
2302
Feature extraction, Annotations, Training, Support vector machines,
Roads, Diseases, Neural networks, Pavement crack detection,
convolutional neural network
BibRef
Lin, C.M.[Chun-Mian],
Tian, D.X.[Da-Xin],
Duan, X.T.[Xu-Ting],
Zhou, J.S.[Jian-Shan],
Zhao, D.Z.[De-Zong],
Cao, D.[Dongpu],
DA-RDD: Toward Domain Adaptive Road Damage Detection Across Different
Countries,
ITS(24), No. 3, March 2023, pp. 3091-3103.
IEEE DOI
2303
Roads, Feature extraction, Training, Surface cracks,
Convolutional neural networks, Annotations, Adaptation models,
intelligent transportation systems
BibRef
Deng, L.[Lu],
Zhang, A.[An],
Guo, J.J.[Jing-Jing],
Liu, Y.[Yingkai],
An Integrated Method for Road Crack Segmentation and Surface Feature
Quantification under Complex Backgrounds,
RS(15), No. 6, 2023, pp. 1530.
DOI Link
2304
BibRef
Zhang, C.[Chong],
Chen, Y.[Yang],
Tang, L.[Luliang],
Chu, X.[Xu],
Li, C.K.[Chao-Kui],
CTCD-Net: A Cross-Layer Transmission Network for Tiny Road Crack
Detection,
RS(15), No. 8, 2023, pp. 2185.
DOI Link
2305
BibRef
Fan, L.[Lili],
Cao, D.[Dongpu],
Zeng, C.X.[Chang-Xian],
Li, B.[Bai],
Li, Y.J.[Yun-Jie],
Wang, F.Y.[Fei-Yue],
Cognitive-Based Crack Detection for Road Maintenance: An Integrated
System in Cyber-Physical-Social Systems,
SMCS(53), No. 6, June 2023, pp. 3485-3500.
IEEE DOI
2305
Roads, Maintenance engineering, Metaverse, Visualization,
Real-time systems, Monitoring, Safety, Brain inspired, visual cognition
BibRef
Cao, T.[Ting],
Wang, Y.H.[Yu-Hang],
Liu, S.[Sheng],
Pavement Crack Detection Based on 3D Edge Representation and Data
Communication With Digital Twins,
ITS(24), No. 7, July 2023, pp. 7697-7706.
IEEE DOI
2307
Digital twins, Solid modeling, Image edge detection, Data models,
Feature extraction, Data communication, Digital twins,
fractional differential
BibRef
Khan, M. .A.M.[Md. Al-Masrur],
Harseno, R.W.[Regidestyoko Wasistha],
Kee, S.H.[Seong-Hoon],
Nahid, A.A.[Abdullah-Al],
Development of AI- and Robotics-Assisted Automated
Pavement-Crack-Evaluation System,
RS(15), No. 14, 2023, pp. 3573.
DOI Link
2307
BibRef
Djenouri, Y.[Youcef],
Belhadi, A.[Asma],
Houssein, E.H.[Essam H.],
Srivastava, G.[Gautam],
Lin, J.C.W.[Jerry Chun-Wei],
Intelligent Graph Convolutional Neural Network for Road Crack
Detection,
ITS(24), No. 8, August 2023, pp. 8475-8482.
IEEE DOI
2308
Roads, Feature extraction, Convolutional neural networks, Anomaly detection,
Visualization, Training, Behavioral sciences, SIFT extractor
BibRef
Gao, Z.[Zhi],
Zhao, X.H.[Xu-Hui],
Cao, M.[Min],
Li, Z.[Ziyao],
Liu, K.C.[Kang-Cheng],
Chen, B.M.[Ben M.],
Synergizing Low Rank Representation and Deep Learning for Automatic
Pavement Crack Detection,
ITS(24), No. 10, October 2023, pp. 10676-10690.
IEEE DOI
2310
BibRef
Liu, H.J.[Hua-Jun],
Yang, J.[Jing],
Miao, X.Y.[Xiang-Yu],
Mertz, C.[Christoph],
Kong, H.[Hui],
CrackFormer Network for Pavement Crack Segmentation,
ITS(24), No. 9, September 2023, pp. 9240-9252.
IEEE DOI
2310
BibRef
Xu, C.[Chuan],
Zhang, Q.[Qi],
Mei, L.[Liye],
Chang, X.F.[Xiu-Feng],
Ye, Z.Y.[Zhao-Yi],
Wang, J.J.[Jun-Jian],
Ye, L.[Lang],
Yang, W.[Wei],
Cross-Attention-Guided Feature Alignment Network for Road Crack
Detection,
IJGI(12), No. 9, 2023, pp. 382.
DOI Link
2310
BibRef
Yang, L.[Lei],
Huang, H.Y.[Han-Yun],
Kong, S.Y.[Shu-Yi],
Liu, Y.H.[Yan-Hong],
Yu, H.[Hongnian],
PAF-Net: A Progressive and Adaptive Fusion Network for Pavement Crack
Segmentation,
ITS(24), No. 11, November 2023, pp. 12686-12700.
IEEE DOI
2311
BibRef
Tian, L.[Lin],
Li, Q.Q.[Qing-Quan],
He, L.[Li],
Zhang, D.[Dejin],
Image-Range Stitching and Semantic-Based Crack Detection Methods for
Tunnel Inspection Vehicles,
RS(15), No. 21, 2023, pp. 5158.
DOI Link
2311
BibRef
Zhang, T.J.[Tian-Jie],
Wang, D.L.[Dong-Lei],
Lu, Y.[Yang],
ECSNet: An Accelerated Real-Time Image Segmentation CNN Architecture
for Pavement Crack Detection,
ITS(24), No. 12, December 2023, pp. 15105-15112.
IEEE DOI
2312
BibRef
Li, K.[Kai],
Yang, J.[Jie],
Ma, S.W.[Si-Wei],
Wang, B.[Bo],
Wang, S.S.[Shan-She],
Tian, Y.J.[Ying-Jie],
Qi, Z.Q.[Zhi-Quan],
Rethinking Lightweight Convolutional Neural Networks for Efficient
and High-Quality Pavement Crack Detection,
ITS(25), No. 1, January 2024, pp. 237-250.
IEEE DOI
2402
Performance evaluation, Deconvolution, Databases, Source coding,
Roads, Decoding, Convolutional neural networks, Crack detection,
feature up-sampling
BibRef
Zhang, H.Y.[Hao-Yuan],
Chen, N.[Ning],
Li, M.[Mei],
Mao, S.J.[Shan-Jun],
The Crack Diffusion Model: An Innovative Diffusion-Based Method for
Pavement Crack Detection,
RS(16), No. 6, 2024, pp. 986.
DOI Link
2403
BibRef
Wang, Y.[Yong],
He, Z.L.[Zheng-Long],
Zeng, X.Q.[Xiang-Qiang],
Zeng, J.C.[Jun-Cheng],
Cen, Z.X.[Zong-Xi],
Qiu, L.Y.[Lu-Yang],
Xu, X.W.[Xiao-Wei],
Zhuo, Q.X.[Qun-Xiong],
GGMNet: Pavement-Crack Detection Based on Global Context Awareness
and Multi-Scale Fusion,
RS(16), No. 10, 2024, pp. 1797.
DOI Link
2405
BibRef
Duan, Z.X.[Ze-Xian],
Liu, J.H.[Jia-Hang],
Ling, X.P.[Xin-Peng],
Zhang, J.L.[Jin-Long],
Liu, Z.H.[Zhi-Heng],
ERNet: A Rapid Road Crack Detection Method Using Low-Altitude UAV
Remote Sensing Images,
RS(16), No. 10, 2024, pp. 1741.
DOI Link
2405
BibRef
Cheng, X.[Xu],
He, T.[Tian],
Shi, F.[Fan],
Zhao, M.[Meng],
Liu, X.[Xiufeng],
Chen, S.Y.[Sheng-Yong],
Selective Feature Fusion and Irregular-Aware Network for Pavement
Crack Detection,
ITS(25), No. 5, May 2024, pp. 3445-3456.
IEEE DOI
2405
Feature extraction, Image edge detection, Roads, Lighting, Fuses,
Deep learning, Deep learning, selective feature fusion
BibRef
Bai, S.[Suli],
Yang, L.[Lei],
Liu, Y.H.[Yan-Hong],
Yu, H.[Hongnian],
DMF-Net: A Dual-Encoding Multi-Scale Fusion Network for Pavement
Crack Detection,
ITS(25), No. 6, June 2024, pp. 5981-5996.
IEEE DOI Code:
WWW Link.
2406
Feature extraction, Transformers, Image segmentation,
Task analysis, Roads, Convolutional neural networks, Deep learning,
multi-scale feature learning
BibRef
Li, C.[Chong],
Fan, Z.[Zhun],
Chen, Y.[Ying],
Lin, H.B.[Hui-Biao],
Moretti, L.[Laura],
Loprencipe, G.[Giuseppe],
Sheng, W.H.[Wei-Hua],
Wang, K.C.P.[Kelvin C. P.],
CrackCLF: Automatic Pavement Crack Detection Based on Closed-Loop
Feedback,
ITS(25), No. 6, June 2024, pp. 5965-5980.
IEEE DOI
2406
Feature extraction, Neural networks, Image segmentation,
Generative adversarial networks, Roads, Task analysis, Training,
closed-loop feedback
BibRef
Zheng, W.W.[Wen-Wen],
Jiang, X.Y.[Xiao-Yan],
Fang, Z.J.[Zhi-Jun],
Gao, Y.B.[Yong-Bin],
TV-Net: A Structure-Level Feature Fusion Network Based on Tensor
Voting for Road Crack Segmentation,
ITS(25), No. 6, June 2024, pp. 5743-5754.
IEEE DOI Code:
WWW Link.
2406
Tensors, Image segmentation, Roads, Feature extraction,
Convolutional neural networks, Eigenvalues and eigenfunctions, U-Net
BibRef
Zhang, X.B.[Xue-Bing],
Pei, J.X.[Jun-Xuan],
Sha, X.D.[Xian-Da],
Feng, X.[Xuan],
Hu, X.[Xin],
Chen, C.L.[Chang-La],
Song, Z.C.[Zheng-Chun],
Experimental Co-Polarimetric GPR Survey on Artificial Vertical
Concrete Cracks by the Improved Time-Varying Centroid Frequency
Scheme,
RS(16), No. 12, 2024, pp. 2095.
DOI Link
2406
BibRef
Lei, Q.[Qin],
Zhong, J.[Jiang],
Wang, C.[Chen],
Joint Optimization of Crack Segmentation With an Adaptive Dynamic
Threshold Module,
ITS(25), No. 7, July 2024, pp. 6902-6916.
IEEE DOI
2407
Task analysis, Optimization, Instance segmentation,
Semantic segmentation, Optimized production technology, joint optimization
BibRef
Yuan, Q.[Qi],
Shi, Y.F.[Yu-Feng],
Li, M.Y.[Ming-Yue],
A Review of Computer Vision-Based Crack Detection Methods in Civil
Infrastructure: Progress and Challenges,
RS(16), No. 16, 2024, pp. 2910.
DOI Link
2408
BibRef
Li, Z.[Zhe],
Torbaghan, M.E.[Mehran Eskandari],
Zhang, T.[Tuo],
Qin, X.[Xia],
Li, W.[Wenda],
Li, Y.J.[Yong-Jian],
Zhang, J.[Jiupeng],
An Automated 3D Crack Severity Assessment Using Surface Data for
Improving Flexible Pavement Maintenance Strategies,
ITS(25), No. 9, September 2024, pp. 12490-12503.
IEEE DOI
2409
Surface cracks, Volume measurement,
Surface morphology, Predictive models, crack volume
BibRef
Sun, L.X.[Li-Xiang],
Yang, Y.X.[Yi-Xin],
Yang, Z.[Zaichun],
Zhou, G.X.[Guo-Xiong],
Li, L.J.[Liu-Jun],
DUCTNet: An Effective Road Crack Segmentation Method in UAV Remote
Sensing Images Under Complex Scenes,
ITS(25), No. 9, September 2024, pp. 12682-12695.
IEEE DOI
2409
Feature extraction, Roads, Image segmentation, Data mining,
Interference, Autonomous aerial vehicles, Training, Complex scenes,
UAV remote sensing image
BibRef
Zhang, H.[Hang],
Zhang, A.A.[Allen A.],
Dong, Z.[Zishuo],
He, A.[Anzheng],
Liu, Y.[Yang],
Zhan, Y.[You],
Wang, K.C.P.[Kelvin C. P.],
Robust Semantic Segmentation for Automatic Crack Detection Within
Pavement Images Using Multi-Mixing of Global Context and Local Image
Features,
ITS(25), No. 9, September 2024, pp. 11282-11303.
IEEE DOI
2409
Transformers, Feature extraction, Convolutional neural networks,
Decoding, Training, Surface cracks, Semantic segmentation, graph network
BibRef
Ma, N.[Nachuan],
Fan, R.[Rui],
Xie, L.H.[Li-Hua],
UP-CrackNet: Unsupervised Pixel-Wise Road Crack Detection via
Adversarial Image Restoration,
ITS(25), No. 10, October 2024, pp. 13926-13936.
IEEE DOI
2410
Roads, Image restoration, Training, Anomaly detection, Task analysis,
Semantics, Semantic segmentation, Semantic segmentation,
unsupervised anomaly detection
BibRef
Zhong, J.T.[Jing-Tao],
Ma, Y.[Yuetan],
Zhang, M.M.[Miao-Miao],
Xiao, R.[Rui],
Cheng, G.[Guantao],
Huang, B.S.[Bao-Shan],
A Pavement Crack Translator for Data Augmentation and Pixel-Level
Detection Based on Weakly Supervised Learning,
ITS(25), No. 10, October 2024, pp. 13350-13363.
IEEE DOI
2410
Generative adversarial networks, Image segmentation, Accuracy,
Data augmentation, Image synthesis, Noise, Generators,
pavement crack detection
BibRef
Wang, X.[Xin],
Mao, Z.Y.[Zhao-Yong],
Liang, Z.W.[Zhi-Wei],
Shen, J.[Junge],
Multi-Scale Semantic Map Distillation for Lightweight Pavement Crack
Detection,
ITS(25), No. 10, October 2024, pp. 15081-15093.
IEEE DOI
2410
Feature extraction, Semantics, Knowledge engineering, Accuracy,
Convolutional neural networks, Transportation, Training,
pavement crack detection
BibRef
Li, P.T.[Peng-Tao],
Wang, M.[Meihua],
Fan, Z.[Zhun],
Huang, H.[Han],
Zhu, G.[Guijie],
Zhuang, J.[Jiafan],
OUR-Net: A Multi-Frequency Network With Octave Max Unpooling and
Octave Convolution Residual Block for Pavement Crack Segmentation,
ITS(25), No. 10, October 2024, pp. 13833-13848.
IEEE DOI
2410
Feature extraction, Image segmentation, Convolution,
Image edge detection, Decoding, Surface cracks, Redundancy,
multi-spatial frequency features
BibRef
Liu, C.Q.[Chuan-Qi],
Zhao, J.K.[Jian-Kang],
Zhu, C.G.[Cheng-Guang],
Xia, X.[Xuan],
Long, H.H.[Hai-Hui],
MECFNet: Reconstruct Sharp Image for UAV-Based Crack Detection,
ITS(25), No. 10, October 2024, pp. 15016-15028.
IEEE DOI
2410
Cameras, Image restoration, Task analysis, Transformers, Kernel,
Image reconstruction, Feature extraction, Crack detection,
cross-modal transformer
BibRef
Cheng, H.Y.[Hao-Yuan],
Zhang, B.[Bei],
Zhong, Y.H.[Yan-Hui],
Xu, S.J.[Sheng-Jie],
Quantitative Pixel-Level Segmentation and 3D Reconstruction of
Concealed Cracks in Asphalt Pavements,
ITS(25), No. 11, November 2024, pp. 18136-18152.
IEEE DOI
2411
Ground penetrating radar, Asphalt, Accuracy, Image segmentation,
Finite difference methods, Time-domain analysis,
3D reconstruction
BibRef
Zhang, Y.[Yu],
Zhang, L.[Lin],
Detection of Pavement Cracks by Deep Learning Models of Transformer
and UNet,
ITS(25), No. 11, November 2024, pp. 15791-15808.
IEEE DOI
2411
Transformers, Image segmentation, Task analysis, Computational modeling,
Convolutional neural networks, Accuracy, CNN
BibRef
Shan, J.H.[Jin-Huan],
Jiang, W.[Wei],
Huang, Y.[Yue],
Yuan, D.D.[Dong-Dong],
Liu, Y.[Yaohan],
Unmanned Aerial Vehicle (UAV)-Based Pavement Image Stitching Without
Occlusion, Crack Semantic Segmentation, and Quantification,
ITS(25), No. 11, November 2024, pp. 17038-17053.
IEEE DOI
2411
Accuracy, Semantic segmentation, Autonomous aerial vehicles, Roads,
Inspection, Task analysis, Image stitching, Pavement distress,
unmanned aerial vehicle (UAV)
BibRef
Li, X.R.[Xin-Ran],
Xu, X.Y.[Xiang-Yang],
Yang, H.[Hao],
A Road Crack Detection Model Integrating GLMANet and EFPN,
ITS(25), No. 11, November 2024, pp. 18211-18223.
IEEE DOI
2411
Feature extraction, Roads, Convolution, Image edge detection,
Semantics, Convolutional neural networks, Data mining,
feature pyramid network
BibRef
Ma, M.Y.[Ming-Yang],
Yang, L.[Lei],
Liu, Y.H.[Yan-Hong],
Yu, H.N.[Hong-Nian],
A Transformer-Based Network With Feature Complementary Fusion for
Crack Defect Detection,
ITS(25), No. 11, November 2024, pp. 16989-17006.
IEEE DOI
2411
Feature extraction, Transformers, Task analysis, Image coding, Encoding,
Computational modeling, Convolutional neural networks, crack detection
BibRef
Chen, Z.Z.[Zhuang-Zhuang],
Lu, R.H.[Rong-Hao],
Chen, J.[Jie],
Song, H.B.H.[Hou-Bing Herbert],
Li, J.Q.[Jian-Qiang],
Implicit Gradient-Modulated Semantic Data Augmentation for Deep Crack
Recognition,
ITS(25), No. 11, November 2024, pp. 16084-16095.
IEEE DOI
2411
Semantics, Training, Data augmentation, Task analysis, Deep learning,
Upper bound, Feature extraction, semantic data augmentation
BibRef
Chen, C.J.[Chun-Jiang],
Song, Y.Z.[Yong-Ze],
Shemery, A.[Ammar],
Hampson, K.[Keith],
Dewan, A.[Ashraf],
Zhong, Y.[Yun],
Wu, P.[Peng],
Large Scale Pavement Crack Evaluation Through a Novel Spatial Machine
Learning Approach Considering Geocomplexity,
ITS(25), No. 12, December 2024, pp. 21429-21441.
IEEE DOI
2412
Roads, Inspection, Numerical models, Maintenance, Data models,
Australia, Visualization, Accuracy, Load modeling, Analytical models,
laser scanning
BibRef
Tao, H.J.[Huan-Jie],
Weakly-Supervised Pavement Surface Crack Segmentation Based on Dual
Separation and Domain Generalization,
ITS(25), No. 12, December 2024, pp. 19729-19743.
IEEE DOI
2412
Image segmentation, Annotations, Training, Surface cracks,
Manuals, Image reconstruction, Data models, image-level labels
BibRef
Zhou, W.[Wei],
Huang, H.[Hongpu],
Zhang, H.C.[Han-Cheng],
Wang, C.[Chen],
Teaching Segment-Anything-Model Domain-Specific Knowledge for Road
Crack Segmentation From On-Board Cameras,
ITS(25), No. 12, December 2024, pp. 20588-20601.
IEEE DOI Code:
WWW Link.
2412
Roads, Image segmentation, Cameras, Training, Adaptation models,
Computational modeling, Transfer learning, Inspection, on-board cameras
BibRef
Zhao, S.G.[Shu-Guang],
Yi, W.[Wen],
Shi, J.J.[Jia-Ji],
Jiang, Z.[Zhengru],
Lu, X.C.[Xiao-Chen],
An Innovative Crack Detection Algorithm Based on Efficient Feature
Fusion and Progressive Transfer Learning,
ITS(25), No. 12, December 2024, pp. 21469-21483.
IEEE DOI
2412
Feature extraction, Accuracy, Bridges, Transfer learning, Data models,
Training, Computational modeling, Transformers, transfer learning (TL)
BibRef
Li, H.T.[Hai-Tao],
Peng, T.[Tao],
Qiao, N.G.[Ning-Guo],
Guan, Z.W.[Zhi-Wei],
Feng, X.Y.[Xin-Yun],
Guo, P.[Peng],
Duan, T.T.[Ting-Ting],
Gong, J.F.[Jin-Feng],
CrackTinyNet: A novel deep learning model specifically designed for
superior performance in tiny road surface crack detection,
IET-ITS(18), No. 12, 2024, pp. 2693-2712.
DOI Link
2501
crack detection, object detection, road safety, road traffic
BibRef
Tao, R.[Rui],
Peng, R.[Rui],
Jin, Y.[Yong],
Gong, F.Y.[Fang-Yuan],
Li, B.[Bo],
Automatic Detection of Asphalt Pavement Crack Width Based on Machine
Vision,
ITS(26), No. 1, January 2025, pp. 484-496.
IEEE DOI
2501
Roads, Asphalt, Maintenance, Surface cracks, Noise, Diseases, Training,
Convolutional neural networks, Complexity theory, road maintenance
BibRef
Wang, Z.F.[Zheng-Fang],
Zhu, H.L.[Hong-Liang],
Yang, Y.J.[Yu-Jie],
Jiang, H.[Haonan],
Li, W.H.[Wen-Hao],
Li, B.[Bingrui],
Li, P.[Peng],
Xu, L.[Lei],
Sui, Q.[Qingmei],
Wang, J.[Jing],
A Pavement Crack Registration and Change Identification Method Based
on Unsupervised Deep Neural Network,
ITS(26), No. 1, January 2025, pp. 757-769.
IEEE DOI
2501
Feature extraction, Autonomous aerial vehicles,
Image registration, Inspection, Convolutional neural networks,
multi-temporal UAV images
BibRef
Qu, Z.[Zhong],
Wang, J.D.[Jian-Dong],
Yin, X.H.[Xue-Hui],
A Directional Connectivity Feature Enhancement Network for Pavement
Crack Detection,
ITS(26), No. 1, January 2025, pp. 1039-1054.
IEEE DOI
2501
Feature extraction, Convolution, Semantics, Accuracy, Noise, Topology,
Background noise, Strips, Shape, Digital images, Crack detection,
directional connectivity
BibRef
Yang, L.[Lei],
Ma, M.Y.[Ming-Yang],
Wu, Z.[Zhenlong],
Liu, Y.H.[Yan-Hong],
A Global-Local Fusion Model via Edge Enhancement and Transformer for
Pavement Crack Defect Segmentation,
ITS(26), No. 2, February 2025, pp. 1964-1981.
IEEE DOI Code:
WWW Link.
2502
Image edge detection, Feature extraction, Defect detection,
Transformers, Data mining, Accuracy, Gabor filters, Decoding.
BibRef
Wang, J.[Jin],
Zhang, T.[Tao],
Li, H.[Hao],
Xu, N.[Niuqi],
Chen, Y.Y.[Yan-Yan],
Pixel-Level Assessment of Pavement Cracks With Light-Weight
Convolution Network and Metric-Skeleton Measurement Using
High-Resolution Images From Low-Cost Camera,
ITS(26), No. 2, February 2025, pp. 2503-2513.
IEEE DOI
2502
Roads, Length measurement, Maintenance, Image segmentation, Cameras,
Convolution, Asphalt, Surface cracks, Skeleton, Manuals,
metric-skeleton measurement
BibRef
Hu, X.K.[Xiang-Kun],
Li, H.[Hua],
Feng, Y.X.[Yi-Xiong],
Qian, S.R.[Song-Rong],
Li, J.[Jian],
Li, S.B.[Shao-Bo],
CCDFormer: A dual-backbone complex crack detection network with
transformer,
PR(161), 2025, pp. 111251.
Elsevier DOI
2502
Concrete crack detection, Semantic segmentation, Transformer,
Convolution neural network, UNet
BibRef
Hu, X.[Xiao],
Chen, Q.H.[Qi-Hao],
Liu, X.[Xiuguo],
Deng, G.[Gang],
Chi, C.[Cheng],
Wang, B.[Bin],
ANF-Net: A Refined Segmentation Network for Road Scenes with Multiple
Noises and Various Morphologies of Cracks,
RS(17), No. 6, 2025, pp. 971.
DOI Link
2503
BibRef
Dong, R.[Ruchan],
Xia, J.[Jinwei],
Zhao, J.[Jin],
Hong, L.[Lei],
CL-PSDD: Contrastive Learning for Adaptive Generalized Pavement
Surface Distress Detection,
ITS(26), No. 4, April 2025, pp. 5211-5224.
IEEE DOI
2504
Contrastive learning, Roads, Feature extraction, YOLO,
Unsupervised learning, Adaptation models, Surface cracks, object detection
BibRef
Li, B.C.[Bing-Chao],
Li, Z.[Zihao],
Zong, J.P.[Jian-Ping],
Wang, H.[Huaichao],
Li, N.[Nansha],
Li, H.F.[Hai-Feng],
A Novel Proactive Fault Tolerance Loss Function for Crack
Segmentation,
ITS(26), No. 5, May 2025, pp. 6361-6378.
IEEE DOI
2505
Fault tolerant systems, Fault tolerance, Image segmentation,
Training, Sensitivity, Roads, Adaptation models, loss function
BibRef
Pascucci, N.[Nicole],
Dominici, D.[Donatella],
Habib, A.[Ayman],
LiDAR-Based Road Cracking Detection: Machine Learning Comparison,
Intensity Normalization, and Open-Source WebGIS for Infrastructure
Maintenance,
RS(17), No. 9, 2025, pp. 1543.
DOI Link
2505
BibRef
Chen, P.H.[Po-Hao],
Hsieh, J.W.[Jun-Wei],
Hsieh, Y.K.[Yi-Kuan],
Chang, C.W.[Chuan-Wang],
Huang, D.Y.[Deng-Yuan],
Cross-Scale Overlapping Patch-Based Attention Network for Road Crack
Detection,
ITS(26), No. 6, June 2025, pp. 7587-7599.
IEEE DOI
2506
Feature extraction, Roads, Convolutional neural networks,
Transformers, Accuracy, Surface cracks
BibRef
Guo, X.[Xin],
Tang, W.Z.[Wen-Zhong],
Wang, H.R.[Hao-Ran],
Wang, J.[Jiale],
Wang, S.[Shuai],
Qu, X.L.[Xiao-Lei],
Lin, X.[Xun],
MorFormer: Morphology-Aware Transformer for Generalized Pavement
Crack Segmentation,
ITS(26), No. 6, June 2025, pp. 8219-8232.
IEEE DOI
2506
Feature extraction, Image reconstruction, Morphology,
Background noise, Transformers, Image segmentation, morphology prior
BibRef
Tan, Q.[Qinzhong],
Li, A.[Ao],
Dong, L.[Le],
Dong, W.S.[Wei-Sheng],
Li, X.[Xin],
Shi, G.M.[Guang-Ming],
CDS-Net: Contextual Difference Sensitivity Network for Pixel-Wise
Road Crack Detection,
CirSysVideo(35), No. 6, June 2025, pp. 5223-5235.
IEEE DOI Code:
WWW Link.
2506
Roads, Image segmentation, Feature extraction, Accuracy, Transformers,
Surface cracks, Shape, Sensitivity, Convolution, cross-entropy loss
BibRef
Pang, J.B.[Jun-Biao],
Xiong, B.C.[Bao-Cheng],
Wu, J.Q.[Jia-Qi],
Huang, Q.M.[Qing-Ming],
Modeling Multi-Granularity Context Information Flow for Pavement
Crack Detection,
ITS(26), No. 7, July 2025, pp. 9165-9174.
IEEE DOI Code:
WWW Link.
2507
Feature extraction, Semantics, Heating systems, Context modeling,
Noise, Convolution, Asphalt, Semantic segmentation, YOLO, Training,
spatial structure
BibRef
Duan, L.J.[Li-Juan],
Zeng, J.[Jun],
Pang, J.B.[Jun-Biao],
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
ALKannad, A.A.[Abdulrahman A.],
Smadi, A.A.[Ahmad Al],
Al-Makhlafi, M.[Moeen],
Yang, S.Y.[Shu-Yuan],
Feng, Z.X.[Zhi-Xi],
CrackVisionX: A Fine-Tuned Framework for Efficient Binary Concrete
Crack Detection,
ITS(26), No. 7, July 2025, pp. 10353-10372.
IEEE DOI
2507
Computational modeling, Accuracy, Deep learning, Real-time systems,
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DICTA21(1-8)
IEEE DOI
2201
Training, Bridges, Image resolution, Computational modeling, Roads,
Image edge detection, Digital images, crack detection, ResNet,
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Autonomous Road Pavement Inspection and Defect Analysis for Smart
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IPRIA21(1-5)
IEEE DOI
2201
Deep learning, Visualization, Asphalt, Smart cities, Roads, Inspection,
Safety, Road pavement crack detection,
smart city
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Wu, X.Y.[Xuan-Yi],
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ICPR21(6577-6583)
IEEE DOI
2105
Deep learning, Learning systems, Image segmentation,
Feature extraction, Surface cracks,
concrete crack detection
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Inoue, Y.[Yuki],
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Crack Detection as a Weakly-Supervised Problem: Towards Achieving
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ICPR21(65-72)
IEEE DOI
2105
Annotations, Roads, Semantics, Buildings, Brightness, Detectors, Inspection
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Shokri, P.,
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Niu, B.,
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Application of CEM Algorithm in the Field of Tunnel Crack
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ICIVC20(232-236)
IEEE DOI
2009
Feature extraction, Gabor filters, Road transportation,
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Benz, C.,
Debus, P.,
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Crack Segmentation on UAS-based Imagery using Transfer Learning,
IVCNZ19(1-6)
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2004
Code, Crack Detection.
WWW Link. autonomous aerial vehicles, convolutional neural nets,
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König, J.,
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A Convolutional Neural Network for Pavement Surface Crack
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ICIP19(1460-1464)
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1910
Semantic Segmentation, Attention, Residual Connections, U-Net, Surface Cracks
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Liu, X.Z.[Xiang-Zeng],
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Robust image-based crack detection in concrete structure using
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ICIP17(2304-2308)
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Indexes, Crack detection, concrete structure, guided filter, image enhancement
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Takada, J.,
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Spatial-Temporal Motion Field Analysis for Pixelwise Crack Detection
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Bridges, Concrete, Labeling, Loading, Maintenance engineering, Safety,
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Adaptation models, Data models, Extremities, Image segmentation,
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Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Inspection -- Paint and Printing Quality, Print Analysis .