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 accessAbstract
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
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
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
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.
Cited by
Sorted by citations
Items 201–300 of 1,001
Ghiasi, R., Khan, M. A., Sorrentino, D., Diaine, C., & Malekjafarian, A. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108167 · Cited by 87
Inspecting Method for Defective Casting Products with Convolutional Neural Network (CNN)
Nguyen, T. P., Choi, S.-H., Park, S.-J., Park, S. H., & Yoon, J. (2020) · International Journal of Precision Engineering and Manufacturing-Green Technology · doi:10.1007/s40684-020-00197-4 · Cited by 88
Lei, Y., Zhang, Y., Mi, J., Liu, W., & Liu, L. (2020) · Structural Health Monitoring · doi:10.1177/1475921720923081 · Cited by 87
Li, Y., Hu, H., Zhu, Z., & Zhou, G. (2020) · ACM Transactions on Sensor Networks · doi:10.1145/3397179 · Cited by 86
The application of deep learning in bridge health monitoring: a literature review
Zhang, G.-Q., Wang, B., Li, J., & Xu, Y. L. (2022) · Advances in Bridge Engineering · doi:10.1186/s43251-022-00078-7 · Cited by 93
Tunnel lining crack detection model based on improved YOLOv5
Duan, S., Zhang, M., Qiu, S., Xiong, J., Zhang, H., Li, C., Jiang, Q., & Kou, Y. (2024) · Tunnelling and Underground Space Technology · doi:10.1016/j.tust.2024.105713 · Cited by 85
Vision based pixel-level bridge structural damage detection using a link ASPP network
Deng, W., Mou, Y., Kashiwa, T., Escalera, S., Nagai, K., Nakayama, K., Matsuo, Y., & Prendinger, H. (2020) · Automation in Construction · doi:10.1016/j.autcon.2019.102973 · Cited by 84
Multi-Sensor Data Integration Using Deep Learning for Characterization of Defects in Steel Elements
Psuj, G. (2018) · Sensors · doi:10.3390/s18010292 · Cited by 84
Convolutional Neural Network for Asphalt Pavement Surface Texture Analysis
Zheng, T., Gao, J., Sha, A., Hu, L., & Li, S. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12406 · Cited by 84
SDD-CNN: Small Data-Driven Convolution Neural Networks for Subtle Roller Defect Inspection
Xu, X., Hong, Z., Guo, Z., Wu, X., & Zheng, Z. (2019) · Applied Sciences · doi:10.3390/app9071364 · Cited by 84
Qiu, Z., Wang, S., Zeng, Z., & Yu, D. (2019) · Journal of Electronic Imaging · doi:10.1117/1.jei.28.4.043023 · Cited by 84
Pavement defect detection with fully convolutional network and an uncertainty framework
Zheng, T., Yuan, D., Gao, J., & Wang, Z. (2020) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12533 · Cited by 85
A light weight multi-scale feature fusion steel surface defect detection model based on YOLOv8
Xie, W., Sun, X., & Ma, W. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad296d · Cited by 84
Xu, Y., Bao, Y., Zhang, Y., & Li, H. (2020) · Structural Health Monitoring · doi:10.1177/1475921720921135 · Cited by 83
Improvement of Damage Segmentation Based on Pixel-Level Data Balance Using VGG-Unet
Shi, J., Dang, J., Cui, M., Zuo, R., Shimizu, K., Tsunoda, A., & Suzuki, Y. (2021) · Applied Sciences · doi:10.3390/app11020518 · Cited by 82
Chen, J., Zhang, D., Huang, H., Shadabfar, M., Zhou, M., & Yang, T. Y. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103371 · Cited by 81
Augmented reality for enhanced visual inspection through knowledge-based deep learning
Wang, S.-H., Zargar, S. A., & Yuan, F. (2020) · Structural Health Monitoring · doi:10.1177/1475921720976986 · Cited by 83
Pan, X., & Yang, T. T. Y. (2021) · Comput. Aided Civ. Infrastructure Eng. · doi:10.1111/mice.12797 · Cited by 78
Task-aware meta-learning paradigm for universal structural damage segmentation using limited images
Xu, Y., Fan, Y., Bao, Y., & Li, H. (2023) · Engineering Structures · doi:10.1016/j.engstruct.2023.115917 · Cited by 81
Waqas, A., Kang, D., & Cha, Y. (2023) · Structural Health Monitoring · doi:10.1177/14759217231177314 · Cited by 83
Self-citationPeng, J., Shao, H., Xiao, Y., Cai, B., & Liu, B. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122361 · Cited by 81
Miao, P., & Srimahachota, T. (2021) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2021.123549 · Cited by 80
Zhang, L., Shen, J., & Baijie, Z. (2022) · Earthquake Engineering and Engineering Vibration · doi:10.1007/s11803-022-2074-7 · Cited by 81
Automatic pixel‐level crack detection and evaluation of concrete structures using deep learning
Zhao, W., Liu, Y., Zhang, J., Shao, Y., & Shu, J. (2022) · Structural Control and Health Monitoring · doi:10.1002/stc.2981 · Cited by 79
Deep convolutional neural networks for semantic segmentation of cracks
Wang, J., Liu, Y.-F., Nie, X., & Mo, Y. L. (2021) · Structural Control and Health Monitoring · doi:10.1002/stc.2850 · Cited by 79
Doğan, G., Arslan, M. H., & İlki̇, A. (2023) · Engineering Structures · doi:10.1016/j.engstruct.2023.115629 · Cited by 78
Computing the characteristics of defects in wooden structures using image processing and CNN
Ehtisham, R., Qayyum, W., Camp, C. V., Plevris, V., Mir, J., Khan, Q. U. Z., & Ahmad, A. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.105211 · Cited by 77
Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review
Hou, L., Chen, H., Zhang, G. (., & Wang, X. (2021) · Applied Sciences · doi:10.3390/app11020821 · Cited by 76
Surface Defects Recognition of Wheel Hub Based on Improved Faster R-CNN
Sun, X., Gu, J., Huang, R., Zou, R., & Girón-Palomares, B. (2019) · Electronics · doi:10.3390/electronics8050481 · Cited by 76
Mei, Q., & Gül, M. (2020) · Structural Health Monitoring · doi:10.1177/1475921719896813 · Cited by 77
Liu, T., Xu, H. G., Ragulskis, M., Cao, M., & Ostachowicz, W. M. (2020) · Sensors · doi:10.3390/s20041059 · Cited by 79
Luo, C. L., Yu, L., Yan, J., Li, Z., Ren, P., Bai, X. P., Yang, E., & Liu, Y. (2021) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12686 · Cited by 76
Li, S., & Zhao, X. (2020) · IEEE Access · doi:10.1109/access.2020.3011106 · Cited by 75
Automated crack assessment and quantitative growth monitoring
Kong, S., Fan, J., Liu, Y.-F., Wei, X., & Ma, X. (2020) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12626 · Cited by 75
Bao, N., Zhang, T., Huang, R., Biswal, S., Su, J., & Wang, Y. (2023) · Structural Control and Health Monitoring · doi:10.1155/2023/8899806 · Cited by 75
Multiscale Attention Networks for Pavement Defect Detection
Chen, J., Wen, Y., Nanehkaran, Y. A., Zhang, D., & Zeb, A. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3298391 · Cited by 74
Czerniawski, T., & Leite, F. L. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101131 · Cited by 73
Automating building element detection for deconstruction planning and material reuse: A case study
Gordon, M., Batallé, A., Wolf, C. D., Sollazzo, A., Dubor, A., & Wang, T. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104697 · Cited by 81
Coupling Deep Learning and UAV for Infrastructure Condition Assessment Automation
Wu, W., Qurishee, M. A., Owino, J., Fomunung, I., Onyango, M., & Atolagbe, B. (2018) · IEEE International Smart Cities Conference (ISC2) · doi:10.1109/isc2.2018.8656971 · Cited by 75
Oyekan, J. O., Farnsworth, M., Hutabarat, W., Miller, D., & Tiwari, A. (2020) · Sensors · doi:10.3390/s20164637 · Cited by 72
A real-time crack detection algorithm for pavement based on CNN with multiple feature layers
Ma, D., Fang, H., Wang, N., Xue, B., Dong, J., & Wang, F. (2021) · Road Materials and Pavement Design · doi:10.1080/14680629.2021.1925578 · Cited by 73
Ye, W., Deng, S., Ren, J., Xu, X.-S., Zhang, K., & Du, W. (2022) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2022.127157 · Cited by 73
Katsamenis, I., Doulamis, N. D., Doulamis, A. D., Protopapadakis, E. E., & Voulodimos, A. S. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104182 · Cited by 72
Modeling automatic pavement crack object detection and pixel-level segmentation
Du, Y., Zhong, S., Fang, H., Wang, N., Liu, C., Wu, D., Sun, Y. P., & Xiang, M. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.104840 · Cited by 72
Kusche, C. F., Reclik, T., Freund, M., Al‐Samman, T., Kerzel, U., & Korte‐Kerzel, S. (2019) · PLoS ONE · doi:10.1371/journal.pone.0216493 · Cited by 74
Unsupervised machine and deep learning methods for structural damage detection: A comparative study
Wang, Z., & Cha, Y. (2022) · Engineering Reports · doi:10.1002/eng2.12551 · Cited by 71
Self-citationQin, R., Zhang, Z., Hu, Z., Du, Z., Xiang, X., Wen, G., & He, W. (2022) · Journal of Materials Processing Technology · doi:10.1016/j.jmatprotec.2022.117851 · Cited by 71
Yang, S., Wang, Z., Wang, J., Cohn, A. G., Zhang, J., Jiang, P., Nie, L., & Sui, Q. (2021) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2021.125658 · Cited by 70
Gong, H., Deng, X., Liu, J., & Huang, J. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.104009 · Cited by 70
Assessment and Use of Unmanned Aerial Vehicle for Civil Structural Health Monitoring
Sreenath, S., Malik, H., Narman, H. S., & Kalaichelavan, K. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.03.174 · Cited by 68
FFEDN: Feature Fusion Encoder Decoder Network for Crack Detection
Liu, C., Zhu, C., Xia, X., Zhao, J., & Long, H. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3141827 · Cited by 68
Online Detection of Surface Defects Based on Improved YOLOV3
Chen, X., Lv, J., Fang, Y., & Du, S. (2022) · Sensors · doi:10.3390/s22030817 · Cited by 70
Global sensitivity analysis for studying hot-mix asphalt dynamic modulus parameters
Owais, M., & Moussa, G. (2024) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2023.134775 · Cited by 73
Zhao, H., Ding, Y., Li, A., Ren, Z., & Yang, K. (2019) · Structural Health Monitoring · doi:10.1177/1475921719875630 · Cited by 67
Shim, S., Kim, J., Cho, G.-C., & Lee, S.-W. (2020) · IEEE Access · doi:10.1109/access.2020.3022786 · Cited by 67
Structural Crack Detection from Benchmark Data Sets Using Pruned Fully Convolutional Networks
Ye, X., Jin, T., Li, Z. X., Ma, S. Y., Ding, Y., & Ou, Y. H. (2021) · Journal of Structural Engineering · doi:10.1061/(asce)st.1943-541x.0003140 · Cited by 70
Review of Prediction of Stress Corrosion Cracking in Gas Pipelines Using Machine Learning
Hussain, M., Zhang, T., Chaudhry, M. Q., Jamil, I., Kausar, S., & Hussain, I. (2024) · Machines · doi:10.3390/machines12010042 · Cited by 67
Pavement crack instance segmentation using YOLOv7-WMF with connected feature fusion
Ye, G., Li, S., Zhou, M., Mao, Y., Qu, J., Shi, T., & Jin, Q. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105331 · Cited by 67
Ramalingam, B., Vega-Heredia, M., Elara, M. R., Vengadesh, A., Lakshmanan, A. K., Ilyas, M., & James, T. J. Y. (2019) · International Journal of Aerospace Engineering · doi:10.1155/2019/5137139 · Cited by 67
The investigation into the failure criteria of concrete based on the BP neural network
Wang, Z., Chen, Q., Wang, Z., & Xiong, J. (2022) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2022.108835 · Cited by 66
Lee, S.-Y., Le, T. H. M., & Kim, Y. (2022) · Developments in the Built Environment · doi:10.1016/j.dibe.2022.100109 · Cited by 66
An Improved Approach to Detection of Rice Leaf Disease with GAN-Based Data Augmentation Pipeline
Haruna, Y., Qin, S., & Kiki, M. J. M. (2022) · Applied Sciences · doi:10.2139/ssrn.4135061 · Cited by 66
Deep Learning Neural Networks for sUAS-Assisted Structural Inspections: Feasibility and Application
Dorafshan, S., Thomas, R. J., Coopmans, C., & Maguire, M. (2018) · International Conference on Unmanned Aircraft Systems (ICUAS) · doi:10.1109/icuas.2018.8453409 · Cited by 69
Steel bridge corrosion inspection with combined vision and thermographic images
Lim, H. J., Hwang, S., Kim, H., & Hoon Sohn, A. (2021) · Structural Health Monitoring · doi:10.1177/1475921721989407 · Cited by 65
Ali, L., Alnajjar, F., Khan, W., Serhani, M. A., & Jassmi, H. A. (2022) · Buildings · doi:10.3390/buildings12040432 · Cited by 68
Mondal, T. G., & Chen, G. (2022) · Frontiers in Built Environment · doi:10.3389/fbuil.2022.1007886 · Cited by 68
Improved YOLOV5-Based UAV Pavement Crack Detection
Xing, J., Liu, Y., & Zhang, G.-Z. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3281585 · Cited by 65
Idriss, L. K., & Owais, M. (2023) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2023.134498 · Cited by 68
Edge‐Enhanced Matching for Gradient‐Based Computer Vision Displacement Measurement
Luo, L., & Feng, M. Q. (2018) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12415 · Cited by 65
Li, G., Liu, Q., Ren, W., Qiao, W., Ma, B., & Wan, J. (2020) · Measurement · doi:10.1016/j.measurement.2020.108693 · Cited by 65
A robotics and computer-aided procedure for defect evaluation in bridge inspection
Potenza, F., Rinaldi, C., Ottaviano, E., & Gattulli, V. (2020) · Journal of Civil Structural Health Monitoring · doi:10.1007/s13349-020-00395-3 · Cited by 64
Guo, B., Wang, Y., Zhen, S., Yu, R., & Su, Z. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3233674 · Cited by 65
Xu, G., Yue, Q., & Liu, X. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102214 · Cited by 66
Khatir, A., Capozucca, R., Khatir, S., Magagnini, E., Thanh, C. L., & Riahi, M. K. (2025) · Journal of the Brazilian Society of Mechanical Sciences and Engineering · doi:10.1007/s40430-025-05697-5 · Cited by 65
Deep Learning for Detecting Building Defects Using Convolutional Neural NetworksPreprint
Perez, H., Tah, J. H. M., & Mosavi, A. (2019) · Preprints.org · doi:10.20944/preprints201908.0068.v1 · Cited by 65
A Light-Weight Deep-Learning Model with Multi-Scale Features for Steel Surface Defect Classification
Liu, Y., Yuan, Y., Balta, C., & Liu, J. (2020) · Materials · doi:10.3390/ma13204629 · Cited by 62
A night pavement crack detection method based on image‐to‐image translation
Liu, C., & Xu, B. (2022) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12849 · Cited by 62
Visual structural inspection datasets
Bianchi, E., & Hebdon, M. H. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104299 · Cited by 64
Cementitious phase quantification using deep learning
Sheiati, S., Nguyen, H., Kinnunen, P., & Ranjbar, N. (2023) · Cement and Concrete Research · doi:10.1016/j.cemconres.2023.107231 · Cited by 62
A detection network for small defects of steel surface based on YOLOv7
Gao, S., Chu, M., & Zhang, L. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104484 · Cited by 62
Kong, X., & Li, J. (2019) · Automation in Construction · doi:10.1016/j.autcon.2018.12.011 · Cited by 51
An ensemble machine learning-based modeling framework for analysis of traffic crash frequency
Zhang, X., Waller, S. T., & Jiang, P. (2019) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12485 · Cited by 62
Structural engineering from an inverse problems perspective
Gallet, A., Rigby, S. E., Tallman, T. N., Kong, X., Hajirasouliha, I., Liew, A., Liu, D., Chen, L., Hauptmann, A., & Smyl, D. (2022) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2021.0526 · Cited by 66
Sim-YOLOv5s: A method for detecting defects on the end face of lithium battery steel shells
Hu, H., & Zhu, Z. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101824 · Cited by 62
Bai, Z., Liu, T., Zou, D., Zhang, M., Zhou, A., & Li, Y. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.104839 · Cited by 61
Vilar-Andreu, M., García, L., García-Sánchez, A.-J., Asorey-Cacheda, R., & Garcı́a-Haro, J. (2024) · IEEE Access · doi:10.1109/access.2024.3413979 · Cited by 61
Automated wireless monitoring system for cable tension forces using deep learning
Jeong, S., Kim, H., Lee, J., & Sim, S. (2020) · Structural Health Monitoring · doi:10.1177/1475921720935837 · Cited by 60
Chen, J., & Liu, D. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101205 · Cited by 60
Yang, Q., & Ji, X. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3089718 · Cited by 60
Automatic damage detection using anchor-free method and unmanned surface vessel
He, Z., Jiang, S., Zhang, J., & Wu, G. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.104017 · Cited by 63
Multiattribute multitask transformer framework for vision‐based structural health monitoring
Gao, Y., Yang, J., Qian, H., & Mosalam, K. M. (2023) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13067 · Cited by 61
Online monitoring of crack dynamic development using attention-based deep networks
Chen, W., He, Z., & Zhang, J. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.105022 · Cited by 60
DLS -GAN: Generative Adversarial Nets for Defect Location Sensitive Data Augmentation
Li, W., Gu, C., Chen, J., Ma, C., Zhang, X., Chen, B., & Wan, S. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3309629 · Cited by 60
Jiang, T., Li, L., Samali, B., Yu, Y., Huang, K., Yan, W., & Wang, L. (2024) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.13219 · Cited by 62
Li, P., Wang, M., Fan, Z., Huang, H. P., Zhu, G., & Zhuang, J. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3405995 · Cited by 61
Kim, H., & Sim, S. (2019) · Structural Control and Health Monitoring · doi:10.1002/stc.2436 · Cited by 60
Deep Regression Neural Network for Industrial Surface Defect Detection
He, Z., & Liu, Q. (2020) · IEEE Access · doi:10.1109/access.2020.2975030 · Cited by 61
Region-Based CNN Method with Deformable Modules for Visually Classifying Concrete Cracks
Deng, L., Chu, H.-H., Shi, P., Wang, W., & Kong, X. (2020) · Applied Sciences · doi:10.3390/app10072528 · Cited by 60
Moussa, G., & Owais, M. (2021) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2021.123589 · Cited by 61
Wang, C., Antos, S. E., & Triveno, L. M. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103968 · Cited by 59
Metadata
- 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
- 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)
- 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
- 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