Semi-supervised semantic segmentation using cross-consistency training for pavement crack detection

References

• Ali, R., Chuah, J. H., Talip, M. S. A., Mokhtar, N., & Shoaib, M. A. (2022). Structural crack detection using deep convolutional neural networks. Automation in Construction, 133. https://doi.org/10.1016/j.autcon.2021.103989
   Web of Science ®Google Scholar
• Alipour, M., & Harris, D. K. (2020). Increasing the robustness of material-specific deep learning models for crack detection across different materials. Engineering Structures, 206. https://doi.org/10.1016/j.engstruct.2019.110157
   Web of Science ®Google Scholar
• Cha, Y.-J., Choi, W., & Büyüköztürk, O. (2017). Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 32(5), 361–378. https://doi.org/10.1111/mice.12263
   Web of Science ®Google Scholar
• Cha, Y.-J., Choi, W., Suh, G., Mahmoudkhani, S., & Büyüköztürk, O. (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. https://doi.org/10.1111/mice.12334
   Web of Science ®Google Scholar
• Chen, L.-C., Papandreou, G., Schroff, F., & Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. ArXiv, abs/1706.05587.
   Google Scholar
• Chen, N., Xu, Z., Liu, Z., Chen, Y., Miao, Y., Li, Q., Hou, Y., & Wang, L. (2022). Data augmentation and intelligent recognition in pavement texture using a deep learning. IEEE Transactions on Intelligent Transportation Systems, 23(12), 25427–25436. https://doi.org/10.1109/tits.2022.3140586
   Web of Science ®Google Scholar
• Chun, P.-J., Izumi, S., & Yamane, T. (2020). Automatic detection method of cracks from concrete surface imagery using two-step light gradient boosting machine. Computer-Aided Civil and Infrastructure Engineering, 36(1), 61–72. https://doi.org/10.1111/mice.12564
   Web of Science ®Google Scholar
• Cuypers, S., Bassier, M., & Vergauwen, M. (2021). Deep learning on construction sites: A case study of sparse data learning techniques for rebar segmentation. Sensors, 21(16). https://doi.org/10.3390/s21165428
   PubMed Web of Science ®Google Scholar
• Dais, D., Bal, İ. E., Smyrou, E., & Sarhosis, V. (2021). Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning. Automation in Construction, 125. https://doi.org/10.1016/j.autcon.2021.103606
   Web of Science ®Google Scholar
• Hayashi, M., Kawakami, T., Adachi, S., Wang, J., Sakai, K., Kiwa, T., Tsukada, K., Miyamoto, Y., Hirohata, M., & Ishikawa, T. (2022). Crack detection for welded joint with surface coating using unsaturated AC magnetic flux leakage. IEEE Transactions on Magnetics, 58(8), 1–5. https://doi.org/10.1109/tmag.2022.3151656
   Web of Science ®Google Scholar
• Hou, Y., Liu, S., Cao, D., Peng, B., Liu, Z., Sun, W., & Chen, N. (2022). A deep learning method for pavement crack identification based on limited field images. IEEE Transactions on Intelligent Transportation Systems, 23(11), 22156–22165. https://doi.org/10.1109/tits.2022.3160524
   Web of Science ®Google Scholar
• Jahangiri, B., Rath, P., Majidifard, H., Urra, L., & Buttlar, W. G. (2021). Investigation of recycled asphalt mixtures in Missouri: Laboratory, field, and ILLI-TC modelling. Road Materials and Pavement Design, 23(6), 1345–1369. https://doi.org/10.1080/14680629.2021.1888778
   Web of Science ®Google Scholar
• Ji, A., Xue, X., Wang, Y., Luo, X., & Xue, W. (2020). An integrated approach to automatic pixel-level crack detection and quantification of asphalt pavement. Automation in Construction, 114. https://doi.org/10.1016/j.autcon.2020.103176
   Web of Science ®Google Scholar
• Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2020). Focal loss for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2), 318–327. https://doi.org/10.1109/TPAMI.2018.2858826
   PubMed Web of Science ®Google Scholar
• Liu, J., Yang, X., Lau, S., Wang, X., Luo, S., Lee, V. C.-S., & 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. https://doi.org/10.1111/mice.12622
   Web of Science ®Google Scholar
• Liu, J., Zhang, J., Ding, Y., Xu, X., Jiang, M., & Shi, Y. (2020). Binarizing weights wisely for edge intelligence: Guide for partial binarization of deconvolution-based generators. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 39(12), 4748–4759. https://doi.org/10.1109/tcad.2020.2983370
   Web of Science ®Google Scholar
• Liu, Y., & Yeoh, J. K. W. (2021). Robust pixel-wise concrete crack segmentation and properties retrieval using image patches. Automation in Construction, 123. https://doi.org/10.1016/j.autcon.2020.103535
   Web of Science ®Google Scholar
• Liu, Z., Cao, Y., Wang, Y., & Wang, W. (2019). Computer vision-based concrete crack detection using U-net fully convolutional networks. Automation in Construction, 104, 129–139. https://doi.org/10.1016/j.autcon.2019.04.005
   Web of Science ®Google Scholar
• Majidifard, H., Adu-Gyamfi, Y., & Buttlar, W. G. (2020). Deep machine learning approach to develop a new asphalt pavement condition index. Construction and Building Materials, 247. https://doi.org/10.1016/j.conbuildmat.2020.118513
   Web of Science ®Google Scholar
• Majidifard, H., Jahangiri, B., Buttlar, W. G., & Alavi, A. H. (2019). New machine learning-based prediction models for fracture energy of asphalt mixtures. Measurement, 135, 438–451. https://doi.org/10.1016/j.measurement.2018.11.081
   Web of Science ®Google Scholar
• Majidifard, H., Jin, P., Adu-Gyamfi, Y., & Buttlar, W. G. (2020). Pavement image datasets: A new benchmark dataset to classify and densify pavement distresses. Transportation Research Record: Journal of the Transportation Research Board, 2674(2), 328–339. https://doi.org/10.1177/0361198120907283
   Web of Science ®Google Scholar
• Naghavi, S. H., & Pourreza, H. (2018, October 25–26). Real-time object detection and classification for autonomous driving [Paper presentation]. 2018 8th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran.
   Google Scholar
• Nayyeri, F., Hou, L., Zhou, J., & Guan, H. (2018). Foreground–background separation technique for crack detection. Computer-Aided Civil and Infrastructure Engineering, 34(6), 457–470. https://doi.org/10.1111/mice.12428
   Web of Science ®Google Scholar
• Ni, F., Zhang, J., & Chen, Z. (2018). Zernike-moment measurement of thin-crack width in images enabled by dual-scale deep learning. Computer-Aided Civil and Infrastructure Engineering, 34(5), 367–384. https://doi.org/10.1111/mice.12421
   Web of Science ®Google Scholar
• Okazaki, Y., Okazaki, S., Asamoto, S., & Chun, P.-J. (2020). Applicability of machine learning to a crack model in concrete bridges. Computer-Aided Civil and Infrastructure Engineering, 35(8), 775–792. https://doi.org/10.1111/mice.12532
   Web of Science ®Google Scholar
• Ouali, Y., Hudelot, C., & Tami, M. (2020, June 13–19). Semi-supervised semantic segmentation with cross-consistency training [Paper presentation]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.
   Google Scholar
• Pan, Y., Zhang, G., & Zhang, L. (2020). A spatial-channel hierarchical deep learning network for pixel-level automated crack detection. Automation in Construction, 119. https://doi.org/10.1016/j.autcon.2020.103357
   Web of Science ®Google Scholar
• Shim, S., Kim, J., Cho, G. C., & Lee, S. W. (2020). Multiscale and adversarial learning-based semi-supervised semantic segmentation approach for crack detection in concrete structures. IEEE Access, 8, 170939–170950. https://doi.org/10.1109/ACCESS.2020.3022786
   Web of Science ®Google Scholar
• Tong, Z., Gao, J., & Zhang, H. (2017). Recognition, location, measurement, and 3D reconstruction of concealed cracks using convolutional neural networks. Construction and Building Materials, 146, 775–787. https://doi.org/10.1016/j.conbuildmat.2017.04.097
   Web of Science ®Google Scholar
• Wang, W., Hu, W., Wang, W., Xu, X., Wang, M., Shi, Y., Qiu, S., & Tutumluer, E. (2021). Automated crack severity level detection and classification for ballastless track slab using deep convolutional neural network. Automation in Construction, 124. https://doi.org/10.1016/j.autcon.2020.103484
   PubMed Web of Science ®Google Scholar
• Wang, W., & Su, C. (2021). Semi-supervised semantic segmentation network for surface crack detection. Automation in Construction, 128. https://doi.org/10.1016/j.autcon.2021.103786
   Web of Science ®Google Scholar
• Yu, Z., Shen, Y., & Shen, C. (2021). A real-time detection approach for bridge cracks based on YOLOv4-FPM. Automation in Construction, 122. https://doi.org/10.1016/j.autcon.2020.103514
   Web of Science ®Google Scholar
• Zhang, X., Rajan, D., & Story, B. (2019). Concrete crack detection using context-aware deep semantic segmentation network. Computer-Aided Civil and Infrastructure Engineering, 34(11), 951–971. https://doi.org/10.1111/mice.12477
   Web of Science ®Google Scholar
• Zhang, Y., & Yuen, K.-V. (2021). Crack detection using fusion features-based broad learning system and image processing. Computer-Aided Civil and Infrastructure Engineering, 36(12), 1568–1584. https://doi.org/10.1111/mice.12753
   Web of Science ®Google Scholar
• Zhao, S., Zhang, D., Xue, Y., Zhou, M., & Huang, H. (2021). A deep learning-based approach for refined crack evaluation from shield tunnel lining images. Automation in Construction, 132. https://doi.org/10.1016/j.autcon.2021.103934
   Web of Science ®Google Scholar
• Zhou, S., & Song, W. (2020). Concrete roadway crack segmentation using encoder-decoder networks with range images. Automation in Construction, 120. doi:10.1016/j.autcon.2020.103403
   Web of Science ®Google Scholar