Cited by 1,558Infrastructure Maintenance and MonitoringStructural Health Monitoring TechniquesIndustrial Vision Systems and Defect Detection

The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.

Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types

Open access

Cha, Young‐Jin, Choi, Wooram, Suh, Gahyun, Mahmoudkhani, Sadegh, Büyüköztürk, Oral·2018·Computer-Aided Civil and Infrastructure Engineering

DOI 10.1111/mice.12334

Open this paper in CoChat — free account

Abstract

Computer vision-based techniques were developed to overcome the limitations of visual inspection by trained human resources and to detect structural damage in images remotely, but most methods detect only specific types of damage, such as concrete or steel cracks. To provide quasi real-time simultaneous detection of multiple types of damages, a Faster Region-based Convolutional Neural Network (Faster R-CNN)-based structural visual inspection method is proposed. To realize this, a database including 2,366 images (with 500 × 375 pixels) labeled for five types of damages—concrete crack, steel corrosion with two levels (medium and high), bolt corrosion, and steel delamination—is developed. Then, the architecture of the Faster R-CNN is modified, trained, validated, and tested using this database. Results show 90.6%, 83.4%, 82.1%, 98.1%, and 84.7% average precision (AP) ratings for the five damage types, respectively, with a mean AP of 87.8%. The robustness of the trained Faster R-CNN is evaluated and demonstrated using 11 new 6,000 × 4,000-pixel images taken of different structures. Its performance is also compared to that of the traditional CNN-based method. Considering that the proposed method provides a remarkably fast test speed (0.03 seconds per image with 500 × 375 resolution), a framework for quasi real-time damage detection on video using the trained networks is developed.

Integrity signals

Registry

Crossref · 10.1111/mice.12334

Cites

39 of 39 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Fast R-CNN
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599

Flagged first, then by citations

Show all 38 references

Research works printed but not linked: 2

Park, J., Kim, T. & Kim, J. (2015), Image‐based bolt‐loosening detection technique of bolt joint in steel bridges, in Proceedings of the 6th International Conference on Advances in Experimental Structural Engineering (6AESE), Urbana–Champaign, IL, 1–2 August 2015.
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R. & Lecun, Y. (2014), Overfeat: integrated recognition, localization and detection using convolutional networks, in Proceedings of the International Conference on Learning Representations (ICLR2014), Banff, Canada, 14–16 April 2014.

Sorted by citations

Items 901–1,000 of 1,001

Damage assessment of laminated composites using unsupervised autonomous features
Khan, A., & Kim, H. S. (2023) · Journal of Thermoplastic Composite Materials · doi:10.1177/08927057231208970 · Cited by 11
Multilabel Concrete Bridge Damage Classification Using Graph Convolution
Yang, J., Li, H., Zhang, L., Zhao, L., Jiang, S., & Xie, H. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3400345 · Cited by 10
Development of an Underwater Detection Robot for the Structures with Pile Foundation
Zhang, W., Zhu, K., Yang, Z., Ye, Y., Ding, J., & Gan, J. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12071051 · Cited by 10
A boosted deep learning-based approach for near real-time response estimation of structures under ground motion excitations
Kaffashchian, M. J., Salkhordeh, M., & Mohammadi, R. K. (2024) · Structure and Infrastructure Engineering · doi:10.1080/15732479.2024.2396613 · Cited by 10
A graph attention reasoning model for prefabricated component detection
Zhou, M., Ye, G., Yuen, K., Yu, W., & Jin, Q. (2025) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13373 · Cited by 11
Automated intelligent detection system for bridge damages with Fractal-features-based improved YOLOv7
Zhang, Y., Chen, X., & Yan, W. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-03846-w · Cited by 10
GTRS-Net: a lightweight visual segmentation model for road crack detection
Zhang, R., Shi, L., Cui, D., Wu, Y., & Ji, Z. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04184-7 · Cited by 10
Improved YOLOv5-based multi-crack detection in concrete wall surfaces
Philip, R. E., Andrushia, A. D., Anand, N., Mathews, M. E., Naser, M. Z., & Lublóy, É. (2025) · Applications in Engineering Science · doi:10.1016/j.apples.2025.100247 · Cited by 10
Data-driven structural damage monitoring and assessment based on unmanned aerial vehicle images: a survey
Yang, Z., Xu, Y., Song, H., & Kang, Y. (2025) · International Journal of Digital Earth · doi:10.1080/17538947.2025.2528617 · Cited by 10
Attention-guided hybrid learning for accurate defect classification in manufacturing environments
Kasem, M. S., Mahmoud, M., Senussi, M. F., Abdalla, M. I., & Kang, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-31654-2 · Cited by 10
AI-based damage detection in prestressed concrete beams: a vision-integrated deep learning framework for crack localization and severity classification
Nguyen, T. Q., Phan-Vu, P., & Nguyen, P. T.-M. (2026) · Advances in Bridge Engineering · doi:10.1186/s43251-025-00194-0 · Cited by 10
ACRM: Attention Cascade R-CNN with Mix-NMS for Metallic Surface Defect Detection
Fang, J., Tan, X., & Wang, Y. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412424 · Cited by 11
Concrete Bridge Damage Detection Based on Transfer Learning with Small Training Samples
Yang, J., Li, H., Huang, D., & Jiang, S. (2021) · International Conference on Systems and Informatics (ICSAI) · doi:10.1109/icsai53574.2021.9664156 · Cited by 10
Harmonizing motion and contrast vision for robust looming detection
Fu, Q., Li, Z., & Peng, J. (2022) · Array · doi:10.1016/j.array.2022.100272 · Cited by 10
Intelligent detection of fine cracks on sleepers based on improved YOLOv5 model of cascade fusion
Ling, L., Xue, F., Zhang, J., Huan, Y., & Chen, F. (2023) · Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit · doi:10.1177/09544097231159707 · Cited by 10
CNN-based segmentation frameworks for structural component and earthquake damage determinations using UAV images
Saida, T., Rashid, M., Nemoto, Y., Tsukamoto, S., Asai, T., & Nishio, M. (2023) · Earthquake Engineering and Engineering Vibration · doi:10.1007/s11803-023-2174-z · Cited by 10
An adaptively weighted loss-enabled lightweight teacher–student model for real-time railroad inspection on edge devices
Guo, J., Zhang, S., Qian, Y., & Wang, Y. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09038-2 · Cited by 11
CNN-based automated approach to crack-feature detection in steam cycle components
Fei, Z., West, G. M., Murray, P., & Dobie, G. (2023) · International Journal of Pressure Vessels and Piping · doi:10.1016/j.ijpvp.2023.105112 · Cited by 10
Adaptive Multi-Feature Fusion for Vehicle Micro-Motor Noise Recognition Considering Auditory Perception
Zhao, T., Ding, W., Huang, H., & Wu, Y. (2023) · Sound&Vibration · doi:10.32604/sv.2023.044203 · Cited by 10
Leveraging Deep Neural Networks for Estimating Vickers Hardness from Nanoindentation Hardness
Niu, J., Miao, B., Guo, J., Ding, Z., He, Y., Chi, Z., Wang, F., & Ma, X. (2023) · Materials · doi:10.3390/ma17010148 · Cited by 10
Selection of the structural severest design ground motions based on big data and random forest
Long, X., Lu, C., Gu, X., Ma, Y., & Li, Z. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108238 · Cited by 10
Deep learning based water leakage detection for shield tunnel lining
Liu, S., Xu, X., Jeon, G., Chen, J., & He, B. (2024) · Frontiers of Structural and Civil Engineering · doi:10.1007/s11709-024-1071-5 · Cited by 10
Small-sample data-driven lightweight convolutional neural network for asphalt pavement defect identification
Jia, L., Zhang, Q., & Gu, X. (2024) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2024.e03643 · Cited by 10
When Transfer Learning Meets Dictionary Learning: A New Hybrid Method for Fast and Automatic Detection of Cracks on Concrete Surfaces
Chen, S., Wang, Y., Ni, Y., & Zhang, Y. (2024) · Structural Control and Health Monitoring · doi:10.1155/2024/3185640 · Cited by 10
NEW DIRECTIONS IN STRUCTURAL HEALTH MONITORING
Mosalam, K. M., Muin, S., & Gao, Y. (2019) · NED University Journal of Research · doi:10.35453/nedjr-stmech-2019-0006 · Cited by 11
Exposed Aggregate Detection of Stilling Basin Slabs Using Attention U-Net Network
Li, Y., Li, X., Wang, H., Wang, S., Gu, S., & Zhang, H. (2020) · KSCE Journal of Civil Engineering · doi:10.1007/s12205-020-1431-1 · Cited by 10
Residual Strength Evaluation of Corroded Textile-Reinforced Concrete by the Deep Learning-Based Method
Wang, W., Shi, P., Deng, L., Chu, H.-H., & Kong, X. (2020) · Materials · doi:10.3390/ma13143226 · Cited by 10
AN OVERVIEW OF A LEADER JOURNAL IN THE FIELD OF TRANSPORT: A BIBLIOMETRIC ANALYSIS OF “COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING” FROM 2000 TO 2019
Wang, X., Xu, Z., Ge, Z., Zavadskas, E. K., & Skačkauskas, P. (2021) · Transport · doi:10.3846/transport.2020.14140 · Cited by 10
Intelligent vision based wear forecasting on surfaces of machine tool elements
Schlagenhauf, T., & Burghardt, N. (2021) · SN Applied Sciences · doi:10.1007/s42452-021-04839-3 · Cited by 10
Deep-Learning-Based Segmentation of Fresh or Young Concrete Sections from Images of Construction Sites
Mesfin, W. M., Cho, S., Lee, J., Kim, H.-K., & Kim, T. (2021) · Materials · doi:10.3390/ma14216311 · Cited by 10
A Machine Vision-based Fabric Defect Detection Solution for Textile Production Industry Using Object Detection
Xin, S., Zhao, C., & Sun, Y. (2021) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc52312.2021.9601900 · Cited by 10
A Detection Method of Bolts on Axlebox Cover Based on Cascade Deep Convolutional Neural Network
Wang, J., Li, L., Zheng, S., Zhao, S., Chai, X., Peng, L., Qi, W., & Tong, Q. (2022) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2022.022143 · Cited by 10
Advanced Sensing and Structural Health Monitoring
Cha, Y., Kim, Y., & You, T. (2018) · Journal of Sensors · doi:10.1155/2018/7286069 · Cited by 10
Self-citation
Crack detection for wading-concrete structures using water irrigation and electric heating
Chen, J., Zeng, Z., Ying, L., Xiong, F., & Cheng, F. (2023) · Frontiers of Structural and Civil Engineering · doi:10.1007/s11709-022-0926-x · Cited by 9
Deep ensemble learning for quantitative geological fracture analysis using borehole televiewer images
Zhang, Y., Chen, J., Li, Y., & Li, B. (2023) · Journal of Applied Geophysics · doi:10.1016/j.jappgeo.2023.105046 · Cited by 10
Aggregation segregation generative adversarial network (AG-GAN) facilitated multi-scale segregation detection in asphalt pavement paving stage
Yang, H., Ma, T., Huyan, J., Han, C., & Wang, H. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107663 · Cited by 9
Nuclear containment damage detection and visualization positioning based on YOLOv5m-FFC
Yu, J., Xu, Y., Cheng, X., Zhou, J., Pan, P., & Yang, P. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105357 · Cited by 9
Damage Identification in Steel Girders of Highway Bridges Utilizing Vibration Based Methods and Convolution Neural Network in the Presence of Noise
Zalaghi, S., Aziminejad, A., Rahami, H., Moghadam, A. S., & Hosseini, M. H. (2024) · Journal of Nondestructive Evaluation · doi:10.1007/s10921-024-01057-w · Cited by 9
Intelligent crack identification method for high‐rise buildings aided by synthetic environments
Yao, Z., Jiang, S., Wang, S., Wang, J., Liu, H., Narazaki, Y., Cui, J., & Spencer, B. F. (2024) · The Structural Design of Tall and Special Buildings · doi:10.1002/tal.2117 · Cited by 9
Damage identification of a jacket platform based on a hybrid deep learning framework
Xin, S., Qi, Z., Li, Y., Yi, H., & Ziguang, J. (2024) · Structural Health Monitoring · doi:10.1177/14759217241262558 · Cited by 9
Unsupervised PG-DDPM-augmented mixed dataset for training an accurate concrete bridge crack detection model under small samples
Deng, J., Hua, L., Lü, Y., Wang, C., & Che, J. (2024) · Measurement · doi:10.1016/j.measurement.2024.116595 · Cited by 9
CNN-transformer hybrid network for automated segmentation of multiple defects in concrete bridges
Yu, Z., Huang, C., & Li, H. (2025) · Advances in Structural Engineering · doi:10.1177/13694332251345935 · Cited by 9
Deep learning-based 3D image reconstruction and damage mapping using neural radiance fields (Nerfacto)
Kim, G., & Cha, Y. (2025) · Structural Health Monitoring · doi:10.1177/14759217251340416 · Cited by 9
Self-citation
Deep learning-based defect identification in hydraulic structures: A comprehensive review
Cai, M., Wang, H., Lv, X., Liu, Z., & Chen, Y. (2025) · KSCE Journal of Civil Engineering · doi:10.1016/j.kscej.2025.100410 · Cited by 9
Lightweight pavement crack detection model for edge computing devices
Li, Z., Yang, J., Wang, H., Li, X., Li, D., & Wang, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-22092-1 · Cited by 12
Automated Estimation of Exposed Temperature and Strength Changing Ratio for Fire-Damaged Concrete Using Deep Learning Method
Hacıefendioğlu, K., Akbulut, Y. E., Nayır, S., Başağa, H. B., & Altunışık, A. C. (2021) · Experimental Techniques · doi:10.1007/s40799-021-00503-y · Cited by 9
Real-Time and Automatic Detection of Welding Joints Using Deep Learning
Lee, D., Nie, G.-Y., & Han, K. K. (2022) · ASCE Construction Research Congress (CRC) · doi:10.1061/9780784483961.063 · Cited by 9
Energy dissipation‐based material deterioration assessment using random decrement technique and convolutional neural network: A case study of Saigon bridge in Ho Chi Minh City, Vietnam
Pham‐Bao, T., Ngo, N. K., Vuong‐Cong, L., & Nguyen‐Nhat, T. (2022) · Structural Control and Health Monitoring · doi:10.1002/stc.2956 · Cited by 9
Automated corrosion detection in Oddy test coupons using convolutional neural networks
Long, E. R., Bone, A., Breitung, E. M., THICKETT, D. R., & Grau‐Bové, J. (2022) · Heritage Science · doi:10.1186/s40494-022-00778-3 · Cited by 9
Deep Learning-Enriched Stress Level Identification of Pretensioned Rods via Guided Wave Approaches
Zhang, Z. M., Tang, F., Cao, Q., Pan, H. X., Wang, X., & Lin, Z. (2022) · Buildings · doi:10.3390/buildings12111772 · Cited by 9
Domain adapted probabilistic inspection using deep probabilistic segmentation
Andersen, R. E., & Boukas, E. (2023) · Ocean Engineering · doi:10.1016/j.oceaneng.2022.113568 · Cited by 9
Surface Crack Detection using Deep Convolutional Neural Network in Concrete Structures
Rahai, A., Rahai, M., Iraniparast, M., & Ghatee, M. (2022) · IEEE International Conference on Image Processing, Applications and Systems (IPAS) · doi:10.1109/ipas55744.2022.10052790 · Cited by 9
Inspection Robot and Wall Surface Detection Method for Coal Mine Wind Shaft
Tang, C., Gao, E., Li, Y., Li, M., Bai, D., Tang, H., & Zhou, G. (2023) · Applied Sciences · doi:10.3390/app13095662 · Cited by 9
Automatic area estimation of algal blooms in water bodies from UAV images using texture analysis
Rahul, A., Lokesh, G., Goswami, S., Ponnalagu, R. N., & Radhika, S. (2023) · Water Science and Engineering · doi:10.1016/j.wse.2023.08.001 · Cited by 9
Efficient object tracking algorithm based on lightweight Siamese networks
Feng, Z., & Wang, H. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.107976 · Cited by 9
Damage Indexing Method for Shear Critical Tubular Reinforced Concrete Structures Based on Crack Image Analysis
Yang, Y., Chang, C.-H., & Wu, C. (2019) · Sensors · doi:10.3390/s19194304 · Cited by 9
A Novel Feature-spanning Machine Learning Technology for Defect Inspection
Hsu, Y.-C., Kuo, P.-Y., & Huang, W.-S. (2019) · International Microsystems, Packaging, Assembly and Circuits Technology Conference · doi:10.1109/impact47228.2019.9024985 · Cited by 9
Cloud-based ML Technologies for Visual Inspection: A Case Study in Manufacturing
Koppe, T., & Schatz, J. (2021) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2021.124 · Cited by 9
A Deep Learning-Based Framework for Damage Detection With Time Series
Yang, Q., Shen, D., Du, W., & Li, W. (2021) · IEEE Access · doi:10.1109/access.2021.3076436 · Cited by 9
A Semi-Supervised Deep Learning Approach for the Classification of Steel Surface Defects
Mayuravaani, M., & Manivannan, S. (2021) · International Conference on Information and Automation for Sustainability (ICIAfS) · doi:10.1109/iciafs52090.2021.9606143 · Cited by 9
Corrosion Modelling Using Convolutional Neural Networks: A Brief Overview
Idusuyi, N., Samuel, O. J., Olugasa, T. T., Ajide, O. O., & Abu, R. (2022) · Journal of Bio- and Tribo-Corrosion · doi:10.1007/s40735-022-00671-3 · Cited by 9
Identification of Casting Product Surface Quality Using Alex net and Le-net CNN Models
Shanthalakshmi, M., mishra, S., Jananee, V., Perumal, P. N., & Jayakar, S. M. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2335/1/012031 · Cited by 9
Use of Unmanned Aircraft Systems for Bridge Inspection: A Review
Rodríguez, D. A., Granados, D. L. Á., & Vidales, J. A. V. (2021) · DYNA · doi:10.15446/dyna.v88n217.91879 · Cited by 9
A Modified Fully Convolutional Network for Crack Damage Identification Compared with Conventional Methods
Meng, M., Zhu, K., Chen, K., & Qu, H. (2021) · Modelling and Simulation in Engineering · doi:10.1155/2021/5298882 · Cited by 9
Seeking a Sufficient Data Volume for Railway Infrastructure Component Detection with Computer Vision Models
Gosiewska, A., Baran, Z., Baran, M., & Rutkowski, T. (2023) · Sensors · doi:10.3390/s23187776 · Cited by 8
A convolution neural network-based technique for health monitoring of connections of a multi-story 3D steel frame structure
Naresh, M., Kumar, V., & Pal, J. (2024) · Multiscale and Multidisciplinary Modeling Experiments and Design · doi:10.1007/s41939-024-00424-4 · Cited by 8
Crack Segmentation on Underground Mine Tunnel based on Swin Transformer
Gao, Z., Zhou, J., & Zhang, H. (2023) · International Conference on Electronic Information Technology and Computer Engineering (EITCE) · doi:10.1145/3650400.3650428 · Cited by 8
Meta-model structural monitoring with cutting-edge AAE-VMD fusion alongside optimized machine learning methods
Hassani, S. (2024) · Structural Health Monitoring · doi:10.1177/14759217241263954 · Cited by 8
Diagnosis of Surface Defects in Hot-Rolled Steel from Deep Learning Features Using Machine Learning Algorithms
Madhavan, S., Sridharan, N. V., Mahanta, T. K., & Sugumaran, V. (2024) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-024-09744-6 · Cited by 8
DACA-Net: Detail-aware network with contrast attention for locating liquid crystal display defects
Luo, S., Chen, H., & Liu, B. (2024) · Displays · doi:10.1016/j.displa.2024.102913 · Cited by 8
Image-based prediction for enclosure structure deformation in pipe-roof tunnel construction using a physical-guided and generative deep learning method
Zhang, C., Pan, Y., & Chen, J.-J. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106002 · Cited by 8
Quantitative multi-index residual capacities assessment of structural components through deep-learning-based image processing: A proof-of-concept study on masonry walls
Cai, W.-Z., Chen, S., Feng, D., & Taciroğlu, E. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103185 · Cited by 8
Object detection model design for tiny road surface damage
Wu, C., Ye, M., Li, H., & Zhang, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-95502-z · Cited by 8
Pixel-level segmentation of spalling in masonry structures based on deep learning
Wang, T., Zhang, J., Chen, W., Chun, Q., & Sun, J. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2025.120686 · Cited by 8
Optimizing deep learning-driven computer vision for civil infrastructure defect Identification: Challenges and strategies
Fu, R., Huang, Z., Cao, M., Novák, D., Xie, C., & Huang, J. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111521 · Cited by 9
Integrating extended reality and AI-based damage segmentation for near real-time, traceable bridge inspections
Pantoja-Rosero, B. G., & Salamone, S. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106567 · Cited by 8
Qualitative Comparison of 2D and 3D Atmospheric Corrosion Detection Methods
Kerf, T. D., Hasheminejad, N., Blom, J., & Vanlanduit, S. (2021) · Materials · doi:10.3390/ma14133621 · Cited by 8
Road crack segmentation using an attention residual U-Net with generative adversarial learning
Hu, X., Yao, M., & Zhang, D. (2021) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2021473 · Cited by 8
On Salient Concrete Crack Detection Via Improved Yolov5
Agyemang, I. O., Zhang, X., Adjei-Mensah, I., Mawuli, B. C., Agbley, B. L. Y., Fiasam, L. D., & Sey, C. (2021) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip53232.2021.9674153 · Cited by 8
Autonomous Multiple Damage Detection and Segmentation in Structures Using Mask R-CNN
Taher, S. S. A. D., & Dang, J. (2022) · Lecture notes in civil engineering · doi:10.1007/978-3-030-93236-7_45 · Cited by 8
Automated fatigue crack detection of orthotropic steel decks using an improved mask region-based convolutional neural network approach
Zhao, Y., Wang, P., Cai, S., Sun, H., & Wang, Z. (2023) · Advances in Structural Engineering · doi:10.1177/13694332231213460 · Cited by 8
Deep Representation Clustering of Multitype Damage Features Based on Unsupervised Generative Adversarial Network
Li, X. G., Zhang, F., Lei, J., & Xiang, W. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3418413 · Cited by 8
Generation of Synthetic Data for Deep Learning in Manufacturing Quality Control Systems
Werda, M. S., Taibi, H., Kouiss, K., & Chebak, A. (2024) · IEEE Mediterranean Electrotechnical Conference (MELECON) · doi:10.1109/melecon56669.2024.10608616 · Cited by 9
Crack analysis of tall concrete wind towers using an ad-hoc deep multiscale encoder–decoder with depth separable convolutions under severely imbalanced data
Deng, J., Hua, L., Lü, Y., Song, Y., Singh, A., Che, J., & Li, Y. (2024) · Structural Health Monitoring · doi:10.1177/14759217241271000 · Cited by 8
A novel computer vision and point cloud-based approach for accurate structural analysis of a tall irregular timber structure
Xie, F., Pan, X., Yang, T. Y., Ernewein, B., Li, M., & Robinson, D. (2024) · Structures · doi:10.1016/j.istruc.2024.107697 · Cited by 8
Feature Consistency Learning for Anomaly Detection
Li, H., & Hu, J. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3522399 · Cited by 8
A Fast Surface-Defect Detection Method Based on Dense-Yolo Network
Gao, F., Zhu, Q., Shao, G., Su, Y., Yang, J., & Yu, X. (2022) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12407 · Cited by 8
An image-based feature tracking approach for bolt loosening detection in steel connections
Kong, X., & Li, J. (2018) · Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems · doi:10.1117/12.2296609 · Cited by 8
Automated fatigue crack identification through motion tracking in a video stream
Kong, X., & Li, J. (2018) · Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems · doi:10.1117/12.2296602 · Cited by 8
Low-cost deep learning UAV and Raspberry Pi solution to real time pavement condition assessment
Qurishee, M. A. (2019) · Cited by 8
An Improved GANs Model for Steel Plate Defect Detection
Tang, R., & Mao, K. (2020) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/790/1/012110 · Cited by 8
A Novel Compact Convolutional Neural Network for Real-Time Nondestructive Evaluation of Metallic Surfaces
Borwankar, R. M., & Ludwig, R. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.2990541 · Cited by 8
Multi-CrackNet: A Fast Segmentation and Quantification Combined Method for Rock Discontinuity Traces by Capturing an Image
Qiao, L., Deng, N., Li, Q., Zhang, Q., & Hao, J. (2022) · Geofluids · doi:10.1155/2022/1832662 · Cited by 8
YOLO’s multiple-strategy PCB defect detection model
Wang, X., Zhang, H., Liu, Q., Gong, W., Bai, S., & You, H. (2024) · IEEE Multimedia · doi:10.1109/mmul.2024.3359267 · Cited by 8
INFRASTRUCTURE DEGRADATION AND POST-DISASTER DAMAGE DETECTION USING ANOMALY DETECTING GENERATIVE ADVERSARIAL NETWORKS
Tilon, S., Nex, F., Duarte, D., Kerle, N., & George Vosselman, A. (2020) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-v-2-2020-573-2020 · Cited by 8
Lightweight Real-time Detection of Components via a Micro Aerial Vehicle with Domain Randomization Towards Structural Health Monitoring
Agyemang, I. O., Zhang, X., Adjei-Mensah, I., Arhin, J. R., & Agyei, E. (2022) · Periodica Polytechnica Civil Engineering · doi:10.3311/ppci.18689 · Cited by 8
Semantic Segmentation Models for Crack Detection: Using Shelled Unmanned Aerial Vehicle Imagery
Arda, M. S., Aleluya, E. R. M., Cahig, C. Y., Librado, L. G., Sumagayan, M. U., Aldueso, K. M. A., Galangque, C. M. J., & Salaan, C. J. O. (2021) · IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) · doi:10.1109/hnicem54116.2021.9731866 · Cited by 8
A Subpixel Concrete Crack Measurement Method Based on the Partial Area Effect
Zheng, J., Liu, Y., Luo, R., Liu, H., Zhou, Z., & He, J. (2024) · Buildings · doi:10.3390/buildings14010151 · Cited by 7
An early fault warning and diagnosis model based on a two-step analysis strategy for wind turbine bearings
Dong, W. G., Wu, X., Zhang, S., & Song, S. (2024) · Structural Health Monitoring · doi:10.1177/14759217241270978 · Cited by 7
Simulation of semiconductor wafer dicing induced faults on chips and their application as augmentation method for a deep learning based visual inspection system
Friedrich, M., Schlosser, T., & Kowerko, D. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02559-0 · Cited by 7

Authors: Cha, Y., Choi, W., Suh, G., Mahmoudkhani, S., & Büyüköztürk, O.
Affiliations: University of Manitoba (CA)
Massachusetts Institute of Technology (US)
Corresponding author: Cha, Young‐Jin
ORCID: Cha, Young‐Jin
Choi, Wooram
Mahmoudkhani, Sadegh
Büyüköztürk, Oral
Venue: Computer-Aided Civil and Infrastructure Engineering
Publisher: Elsevier BV
Published: 2018-09
Volume: 33
Issue: 9
Pages: 731-747
ISSN: 1093-9687 (ISSN-L)
DOI: 10.1111/mice.12334
OpenAlex: W2768955070
Cited by: 1,558
References: 38
Citations by year: 2026: 107 · 2025: 205 · 2024: 264 · 2023: 264 · 2022: 231 · 2021: 186 · 2020: 160 · 2019: 98 · 2018: 43
FWCI: 86.04
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor, 2753 d)
Locations: Computer-Aided Civil and Infrastructure Engineering (publishedVersion, cc-by-nc-nd)
Topics: Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
Keywords: damage detection, Faster R-CNN, region-based object detection, concrete cracking, steel corrosion, bolt corrosion

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Citations

Cites

Cited by

Related

Metadata

Record

About these checks