|
1. Tupe-Waghmare P, Joshi RR. A Scoping Review of Classification of Concrete Cracks using Deep Convolution Learning Approach. Library Philosophy and Practice. 2021:1-28. 2. da Silva WRL, de Lucena DS, editors. Concrete cracks detection based on deep learning image classification. Proceedings; 2018. [ DOI:10.3390/ICEM18-05387] 3. Cazzato D, Cimarelli C, Sanchez-Lopez JL, Voos H, Leo M. A survey of computer vision methods for 2d object detection from unmanned aerial vehicles. Journal of Imaging. 2020;6(8):78. [ DOI:10.3390/jimaging6080078] 4. Çakıroğlu MA, Süzen AA. Assessment and application of deep learning algorithms in civil engineering. El-Cezeri. 2020;7(2):906-22. 5. Le T-T, Nguyen V-H, Le MV. Development of deep learning model for the recognition of cracks on concrete surfaces. Applied computational intelligence and soft computing. 2021;2021(1):8858545. [ DOI:10.1155/2021/8858545] 6. Yusof N, Ibrahim A, Noor M, Tahir N, Yusof N, Abidin N, et al., editors. Deep convolution neural network for crack detection on asphalt pavement. Journal of Physics: Conference Series; 2019: IOP Publishing. [ DOI:10.1088/1742-6596/1349/1/012020] 7. Carrio A, Sampedro C, Rodriguez-Ramos A, Campoy P. A review of deep learning methods and applications for unmanned aerial vehicles. Journal of Sensors. 2017;2017(1):3296874. [ DOI:10.1155/2017/3296874] 8. Li S, Zhao X. Image‐based concrete crack detection using convolutional neural network and exhaustive search technique. Advances in civil engineering. 2019;2019(1):6520620. [ DOI:10.1155/2019/6520620] 9. Chow JK, Su Z, Wu J, Li Z, Tan PS, Liu K-f, et al. Artificial intelligence-empowered pipeline for image-based inspection of concrete structures. Automation in Construction. 2020;120:103372. [ DOI:10.1016/j.autcon.2020.103372] 10. Dais D, Bal IE, Smyrou E, Sarhosis V. Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning. Automation in Construction. 2021;125:103606. [ DOI:10.1016/j.autcon.2021.103606] 11. Huang J, Wu D, editors. Pavement crack detection method based on deep learning. CIBDA 2022; 3rd International Conference on Computer Information and Big Data Applications; 2022: VDE. 12. Liu H, Lin C, Cui J, Fan L, Xie X, Spencer BF. Detection and localization of rebar in concrete by deep learning using ground penetrating radar. Automation in construction. 2020;118:103279. [ DOI:10.1016/j.autcon.2020.103279] 13. Lee JS, Hwang SH, Choi IY, Choi Y. Estimation of crack width based on shape‐sensitive kernels and semantic segmentation. Structural Control and Health Monitoring. 2020;27(4):e2504. [ DOI:10.1002/stc.2504] 14. Zhang C, Chang Cc, Jamshidi M. Concrete bridge surface damage detection using a single‐stage detector. Computer‐Aided Civil and Infrastructure Engineering. 2020;35(4):389-409. [ DOI:10.1111/mice.12500] 15. Bae H, Jang K, An Y-K. Deep super resolution crack network (SrcNet) for improving computer vision-based automated crack detectability in in situ bridges. Structural Health Monitoring. 2021;20(4):1428-42. [ DOI:10.1177/1475921720917227] 16. Kim B, Cho S. Image‐based concrete crack assessment using mask and region‐based convolutional neural network. Structural Control and Health Monitoring. 2019;26(8):e2381. [ DOI:10.1002/stc.2381] 17. Liang X. Image‐based post‐disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization. Computer‐Aided Civil and Infrastructure Engineering. 2019;34(5):415-30. [ DOI:10.1111/mice.12425] 18. Ni J, Li J, McAuley J, editors. Justifying recommendations using distantly-labeled reviews and fine-grained aspects. Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP); 2019. [ DOI:10.18653/v1/D19-1018] 19. Ali L, Alnajjar F, Jassmi HA, Gocho M, Khan W, Serhani MA. Performance evaluation of deep CNN-based crack detection and localization techniques for concrete structures. Sensors. 2021;21(5):1688. [ DOI:10.3390/s21051688] 20. Shatnawi N. Automatic pavement cracks detection using image processing techniques and neural network. International Journal of Advanced Computer Science and Applications. 2018;9(9):399-402. [ DOI:10.14569/IJACSA.2018.090950] 21. Cha YJ, Choi W, Büyüköztürk O. Deep learning‐based crack damage detection using convolutional neural networks. Computer‐Aided Civil and Infrastructure Engineering. 2017;32(5):361-78. [ DOI:10.1111/mice.12263] 22. Xu H, Su X, Wang Y, Cai H, Cui K, Chen X. Automatic bridge crack detection using a convolutional neural network. Applied Sciences. 2019;9(14):2867. [ DOI:10.3390/app9142867] 23. Fan Z, Li C, Chen Y, Di Mascio P, Chen X, Zhu G, et al. Ensemble of deep convolutional neural networks for automatic pavement crack detection and measurement. Coatings. 2020;10(2):152. [ DOI:10.3390/coatings10020152] 24. Pauly L, Hogg D, Fuentes R, Peel H, editors. Deeper networks for pavement crack detection. Proceedings of the 34th ISARC; 2017: IAARC. [ DOI:10.22260/ISARC2017/0066] 25. Yang X, Li H, Yu Y, Luo X, Huang T, Yang X. Automatic pixel‐level crack detection and measurement using fully convolutional network. Computer‐Aided Civil and Infrastructure Engineering. 2018;33(12):1090-109. [ DOI:10.1111/mice.12412] 26. Zhang A, Wang KC, Fei Y, Liu Y, Tao S, Chen C, et al. Deep learning-based fully automated pavement crack detection on 3D asphalt surfaces with an improved CrackNet. Journal of Computing in Civil Engineering. 2018;32(5):04018041. [ DOI:10.1061/(ASCE)CP.1943-5487.0000775] 27. Elghaish F, Talebi S, Abdellatef E, Matarneh ST, Hosseini MR, Wu S, et al. Developing a new deep learning CNN model to detect and classify highway cracks. Journal of Engineering, Design and Technology. 2022;20(4):993-1014. [ DOI:10.1108/JEDT-04-2021-0192] 28. Kung R-Y, Pan N-H, Wang CC, Lee P-C. Application of deep learning and unmanned aerial vehicle on building maintenance. Advances in Civil Engineering. 2021;2021(1):5598690. [ DOI:10.1155/2021/5598690] 29. Ibrahim DM, Elshennawy NM, Sarhan AM. Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases. Computers in biology and medicine. 2021;132:104348. [ DOI:10.1016/j.compbiomed.2021.104348] 30. Gopalakrishnan K, Khaitan SK, Choudhary A, Agrawal A. Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection. Construction and building materials. 2017;157:322-30. [ DOI:10.1016/j.conbuildmat.2017.09.110] 31. Zhang K, Cheng H-D, Zhang B. Unified approach to pavement crack and sealed crack detection using preclassification based on transfer learning. Journal of Computing in Civil Engineering. 2018;32(2):04018001. [ DOI:10.1061/(ASCE)CP.1943-5487.0000736] 32. Sun S, Wang B. Low-altitude UAV 3D modeling technology in the application of ancient buildings protection situation assessment. Energy Procedia. 2018;153:320-4. [ DOI:10.1016/j.egypro.2018.10.082] 33. Islam MM, Hossain MB, Akhtar MN, Moni MA, Hasan KF. CNN based on transfer learning models using data augmentation and transformation for detection of concrete crack. Algorithms. 2022;15(8):287. [ DOI:10.3390/a15080287] 34. Babaei P, editor Convergence of Deep Learning and Edge Computing using Model Optimization. 2024 13th Iranian/3rd International Machine Vision and Image Processing Conference (MVIP); 2024: IEEE. [ DOI:10.1109/MVIP62238.2024.10491145] 35. Mousavi SM, Hosseini S. A Convolutional Neural Network Model for Detection of COVID-19 Disease and Pneumonia. Journal of Health and Biomedical Informatics. 2023;10(1):41-56. [ DOI:10.34172/jhbmi.2023.13] |
