References
- Al Qurishee, M., Wu, W., Atolagbe, B., Owino, J., Fomunung, I., El Said, S., and Tareq, S.M., 2020. Bridge girder crack assessment using faster rcnn inception v2 and infrared thermography. Journal of Transportation Technologies, 10 (2), 110–127.
- Babajanian Bisheh, H., Ghodrati Amiri, G., and Darvishan, E., 2020. Ensemble classifiers and feature-based methods for structural damage assessment. Shock and Vibration, 2020.
- Cha, Y.J., Choi, W., and Büyüköztürk, O., 2017. Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 32 (5), 361–378.
- Cha, Y.J., Choi, E., and Suh, G., 2018. Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Computer-Aided Civil and Infrastructure Engineering, 33 (9), 731–747.
- Chen, T., Cai, X., Zhao, X., Chen, C., Liang, X., Zou, T., and Wang, P., 2020. Pavement crack detection and recognition using the architecture of segnet. Journal of Industrial Information Integration, 18, 100144.
- Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L., 2017. Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40 (4), 834–848.
- Chen, J.H., Su, M.C., Cao, R., Hsu, S.C., and Lu, J.C., 2017. A self organizing map optimization based image recognition and processing model for bridge crack inspection. Automation in Construction, 73, 58–66.
- Choi, W., and Cha, Y.J., 2019. Sddnet: real-time crack segmentation. IEEE Transactions on Industrial Electronics, 67 (9), 8016–8025.
- Cord, A., and Chambon, S., 2012. Automatic road defect detection by textural pattern recognition based on adaboost. Computer-Aided Civil and Infrastructure Engineering, 27 (4), 244–259.
- Deng, J., Lu, Y., and Lee, V.C.S., 2020. Concrete crack detection with handwriting script interferences using faster region-based convolutional neural network. Computer-Aided Civil and Infrastructure Engineering, 35 (4), 373–388.
- Dong, W., Huang, Y., Lehane, B., and Ma, G., 2020. Xgboost algorithm-based prediction of concrete electrical resistivity for structural health monitoring. Automation in Construction, 114, 103155.
- Dung, C.V., 2019. Autonomous concrete crack detection using deep fully convolutional neural network. Automation in Construction, 99, 52–58.
- Feng, D.C., Liu, Z.T., Wang, X.D., Jiang, Z.M., and Liang, S.X., 2020. Failure mode classification and bearing capacity prediction for reinforced concrete columns based on ensemble machine learning algorithm. Advanced Engineering Informatics, 45, 101126.
- Feng, C., Zhang, H., Wang, S., Li, Y., Wang, H., and Yan, F., 2019. Structural damage detection using deep convolutional neural network and transfer learning. KSCE Journal of Civil Engineering, 23 (10), 4493–4502.
- Gao, Y., and Mosalam, K.M., 2018. Deep transfer learning for image-based structural damage recognition. Computer-Aided Civil and Infrastructure Engineering, 33 (9), 748–768.
- Gou, C., Peng, B., Li, T., and Gao, Z., 2019. Pavement crack detection based on the improved faster-rcnn. 2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE, 962–967.
- Haiyong, C., Peng, Z., and Haowei, Y., 2021. Crack detection based on multi-scale faster rcnn with attention. Opto-Electronic Engineering, 48 (01), 200112.
- Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C., 2020. Ghostnet: More features from cheap operations. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1580–1589.
- He, K., Zhang, X., Ren, S., and Sun, J., 2015. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37 (9), 1904–1916.
- He, M., Zhang, L., Zheng, W., and Feng, Y., 2017. Crack detection based on a moving mode of eddy current thermography method. Measurement, 109, 119–129.
- Hsiel, Y.A., and Tsai, Y.C.J., 2021. Dau-net: Dense attention u-net for pavement crack segmentation. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC). IEEE, 2251–2256.
- Kang, D., Benipal, S.S., Gopal, D.L., and Cha, Y.J., 2020. Hybrid pixel-level concrete crack segmentation and quantification across complex backgrounds using deep learning. Automation in Construction, 118, 103291.
- Koziarski, M., and Cyganek, B., 2017. Image recognition with deep neural networks in presence of noise–dealing with and taking advantage of distortions. Integrated Computer-Aided Engineering, 24 (4), 337–349.
- Lau, S.L., Chong, E.K., Yang, X., and Wang, X., 2020. Automated pavement crack segmentation using u-net-based convolutional neural network. IEEE Access, 8, 114892–114899.
- LeCun, Y., Bengio, Y., and Hinton, G., 2015. Deep learning. Nature, 521 (7553), 436–444.
- Lee, D., Kim, J., and Lee, D., 2019. Robust concrete crack detection using deep learning-based semantic segmentation. International Journal of Aeronautical and Space Sciences, 20 (1), 287–299.
- Li, Y., Che, P., Liu, C., Wu, D., and Du, Y., 2021. Cross-scene pavement distress detection by a novel transfer learning framework. Computer-Aided Civil and Infrastructure Engineering, 36 (11), 1398–1415.
- Li, R., Yuan, Y., Zhang, W., and Yuan, Y., 2018. Unified vision-based methodology for simultaneous concrete defect detection and geolocalization. Computer-Aided Civil and Infrastructure Engineering, 33 (7), 527–544.
- Li, S., and Zhao, X., 2020. Pixel-level detection and measurement of concrete crack using faster region-based convolutional neural network and morphological feature extraction. Measurement Science and Technology, 32, 065010.
- Li, S., Zhao, X., and Zhou, G., 2019. Automatic pixel-level multiple damage detection of concrete structure using fully convolutional network. Computer-Aided Civil and Infrastructure Engineering, 34 (7), 616–634.
- Li, S., Zhou, H., Wang, G., Zhu, X., Kong, L., and Hu, Z., 2018. Cracked insulator detection based on r-fcn. Journal of Physics: Conference Series. IOP Publishing, vol. 1069, 012147.
- Liang, X., 2019. Image-based post-disaster inspection of reinforced concrete bridge systems using deep learning with bayesian optimization. Computer-Aided Civil and Infrastructure Engineering, 34 (5), 415–430.
- Lin, C.S., Chen, S.H., Chang, C.M., and Shen, T.W., 2019. Crack detection on a retaining wall with an innovative, ensemble learning method in a dynamic imaging system. Sensors, 19 (21), 4784.
- Liu, Z., Cao, Y., Wang, Y., and Wang, W., 2019. Computer vision-based concrete crack detection using u-net fully convolutional networks. Automation in Construction, 104, 129–139.
- Liu, J., Yang, X., Lau, S., Wang, X., Luo, S., Lee, V.C., and Ding, L., 2020. Automated pavement crack detection and segmentation based on two-step convolutional neural network. Computer-Aided Civil and Infrastructure Engineering, 35 (11), 1291–1305.
- Liu, Y., Yao, J., Lu, X., Xie, R., and Li, L., 2019. Deepcrack: A deep hierarchical feature learning architecture for crack segmentation. Neurocomputing, 338, 139–153.
- Mariniello, G., Pastore, T., Menna, C., Festa, P., and Asprone, D., 2020. Structural damage detection and localization using decision tree ensemble and vibration data. Computer-Aided Civil and Infrastructure Engineering, 36, 1129–1149.
- Nabian, M.A., and Meidani, H., 2018. Deep learning for accelerated seismic reliability analysis of transportation networks. Computer-Aided Civil and Infrastructure Engineering, 33 (6), 443–458.
- Nejad, F.M., and Zakeri, H., 2011. An optimum feature extraction method based on wavelet–radon transform and dynamic neural network for pavement distress classification. Expert Systems with Applications, 38 (8), 9442–9460.
- Oliveira, H., and Correia, P.L., 2014. Crackit – an image processing toolbox for crack detection and characterization. IOP Conference Series: Earth and Environmental Science. IEEE, 798–802.
- Pan, X., and Yang, T., 2020. Postdisaster image-based damage detection and repair cost estimation of reinforced concrete buildings using dual convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 35 (5), 495–510.
- Pan, Y., Zhang, G., and Zhang, L., 2020. A spatial-channel hierarchical deep learning network for pixel-level automated crack detection. Automation in Construction, 119, 103357.
- Perez-Ramirez, C.A., Amezquita-Sanchez, J.P., Valtierra-Rodriguez, M., Adeli, H., Dominguez-Gonzalez, A., and Romero-Troncoso, R.J., 2019. Recurrent neural network model with bayesian training and mutual information for response prediction of large buildings. Engineering Structures, 178, 603–615.
- Redmon, J., 2016. You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition. IOP Publishing, 779–788.
- Redmon, J., and Farhadi, A., 2017. Yolo9000: better, faster, stronger. Proceedings of the IEEE conference on computer vision and pattern recognition. 7263–7271.
- Ren, S., He, K., Girshick, R., and Sun, J., 2016. Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 28.
- Sari, Y., Prakoso, P.B., and Baskara, A.R., 2019. Road crack detection using support vector machine (svm) and otsu algorithm. 2019 6th International Conference on Electric Vehicular Technology (ICEVT). IEEE, 349–354.
- Shen, C., Liu, L., Zhu, L., Kang, J., Wang, N., and Shao, L., 2020. High-throughput in situ root image segmentation based on the improved deeplabv3+ method. Frontiers in Plant Science, 11, 1565.
- Shi, Y., Cui, L., Qi, Z., Meng, F., and Chen, Z., 2016. Automatic road crack detection using random structured forests. IEEE Transactions on Intelligent Transportation Systems, 17 (12), 3434–3445.
- Silva, M., Santos, A., Figueiredo, E., Santos, R., Sales, C., and Costa, J.C.W.A., 2016. A novel unsupervised approach based on a genetic algorithm for structural damage detection in bridges. Engineering Applications of Artificial Intelligence, 52, 168–180.
- Sun, Y., Yang, Y., Yao, G., Wei, F., and Wong, M., 2021. Autonomous crack and bughole detection for concrete surface image based on deep learning. IEEE Access, 9, 85709–85720.
- Tan, M., and Le, Q., 2019. Efficientnet: Rethinking model scaling for convolutional neural networks. International Conference on Machine Learning. PMLR, 6105–6114.
- Wan, H., Gao, L., Su, M., Sun, Q., and Huang, L., 2021. Attention-based convolutional neural network for pavement crack detection. Advances in Materials Science and Engineering, 2021.
- Wang, P., and Bai, X., 2018. Regional parallel structure based cnn for thermal infrared face identification. Integrated Computer-Aided Engineering, 25 (3), 247–260.
- Wang, S., Yang, F., Cheng, Y., Yang, Y., and Wang, Y., 2018. Adaboost-based crack detection method for pavement. IOP Conference Series: Earth and Environmental Science. IOP Publishing, vol. 189, 022005.
- Woo, S., Park, J., Lee, J.Y., and Kweon, I.S., 2018. Cbam: Convolutional block attention module. Proceedings of the European conference on computer vision (ECCV). 3–19.
- Yao, G., Sun, Y., Yang, Y., and Liao, G., 2021. Lightweight neural network for real-time crack detection on concrete surface in fog. Frontiers in Materials, 8, 517.
- Yeum, C.M., Choi, J., and Dyke, S.J., 2019. Automated region-of-interest localization and classification for vision-based visual assessment of civil infrastructure. Structural Health Monitoring, 18 (3), 675–689.
- Yeum, C.M., and Dyke, S.J., 2015. Vision-based automated crack detection for bridge inspection. Computer-Aided Civil and Infrastructure Engineering, 30 (10), 759–770.
- Yoo, H.S., and Kim, Y.S., 2016. Development of a crack recognition algorithm from non-routed pavement images using artificial neural network and binary logistic regression. KSCE Journal of Civil Engineering, 20 (4), 1151–1162.
- Zalama, E., Gómez-García-Bermejo, J., Medina, R., and Llamas, J., 2014. Road crack detection using visual features extracted by gabor filters. Computer-Aided Civil and Infrastructure Engineering, 29 (5), 342–358.
- Zhang, A., Wang, K.C.P., Ji, R., and Li, Q.J., 2016. Efficient system of cracking-detection algorithms with 1-mm 3d-surface models and performance measures. Journal of Computing in Civil Engineering, 30 (6), 04016020.
- Zhang, A., Wang, K.C.P., Li, B., Yang, E., Dai, X., Peng, Y., Fei, Y., Liu, Y., Li, J.Q., and Chen, C., 2017. Automated pixel-level pavement crack detection on 3d asphalt surfaces using a deep-learning network. Computer-Aided Civil and Infrastructure Engineering, 32 (10), 805–819.
- Zhang, W., Zhang, Z., Qi, D., and Liu, Y., 2014. Automatic crack detection and classification method for subway tunnel safety monitoring. Sensors, 14 (10), 19307–19328.