A new global multiplexing structure of original features for road crack detection

References

• Alzubaidi, F., 2021. Automated lithology classification from drill core images using convolutional neural networks. Journal of Petroleum Science and Engineering, 179, 107933.
   Google Scholar
• Amhaz, R., et al., 2016. Automatic crack detection on 2d pavement images: An algorithm based on minimal path selection. IEEE Transactions on Intelligent Transportation Systems, 17 (10), 2718–2729.
   Web of Science ®Google Scholar
• Badrinarayanan, V., Kendall, A., and Cipolla, R., 2017. Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39 (12), 2481–2495.
   PubMed Web of Science ®Google Scholar
• Çelik, F., and König, M., 2022. A sigmoid optimized encoder decoder network for crack segmentation with copy-edit-paste transfer learning. Computer-Aided Civil and Infrastructure Engineering, 37 (14), 1875–1890.
   Web of Science ®Google Scholar
• Chen, L.-C., et al., 2014. Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv.
   Google Scholar
• Chen, L.-C., et al., 2017a. 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.
   PubMed Web of Science ®Google Scholar
• Chen, L.-C., et al., 2017b. Rethinking atrous convolution for semantic image segmentation. arXiv.
   Google Scholar
• Cui, L., et al., 2015. Pavement distress detection using random decision forestsed. International Conference on Data Science, University of Technology Sydney, Sydney, Australia: Springer, 95–102.
   Google Scholar
• Dorafshan, S., Thomas, R.J., and Maguire, M., 2018. Sdnet2018: An annotated image dataset for non-contact concrete crack detection using deep convolutional neural networks. Data in Brief, 21, 1664–1668.
   PubMedGoogle Scholar
• Dung, C.V., and Anh, L.D., 2019. Autonomous concrete crack detection using deep fully convolutional neural network. Automation in Construction, 99, 52–58.
   Web of Science ®Google Scholar
• Eisenbach, Markus, et al., 2017. How to get pavement distress detection ready for deep learning? A systematic approach.
   Google Scholar
• Gao, S., et al., 2019. Res2net: a new multi-scale backbone architecture. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43 (2), 652–662.
   Web of Science ®Google Scholar
• Han, K., et al., 2019. Ghostnet: more features from cheap operations. arXiv.
   Google Scholar
• Han, Song, Mao, H., and Dally, W. J., 2015. Deep compression: compressing deep neural networks with pruning, trained quantization and Huffman coding. Fiber, 56 (4), 3–7.
   Google Scholar
• He, K., et al., 2016a. Deep residual learning for image recognitioned. In: IEEE conference on computer vision and pattern recognition Seattle, 770–778.
   Google Scholar
• He, Kaiming, et al., 2016b. Identity mappings in deep residual networks. In: 14th European Conference on Computer Vision (ECCV2016), 630–645.
   Google Scholar
• Huang, G.L., Liu, Zhuang, and Van Der Maaten, Laurens, 2017. Densely connected convolutional networksed. In: 30TH IEEE conference on computer vision and pattern recognition (CVPR 2017), 2261–2269.
   Google Scholar
• Huang, H., et al., 2020. Unet 3 + a full-scale connected unet for medical image segmentationed. In: IEEE International Conference on Acoustics, 1055–1059.
   Google Scholar
• Krizhevsky, A., Sutskever, I., and Hinton, G.E., 2017. Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60 (6), 84–90.
   Web of Science ®Google Scholar
• Li, B., et al., 2018. Automatic classification of pavement crack using deep convolutional neural network. International Journal of Pavement Engineering, 21 (4), 457–463.
   Web of Science ®Google Scholar
• Li, G., et al., 2020. Automatic bridge crack identification from concrete surface using resnext with postprocessing. Structural Control and Health Monitoring, 27 (11), e2620.
   Web of Science ®Google Scholar
• Liu, C., et al., 2022. Ffedn: feature fusion encoder decoder network for crack detection. IEEE Transactions on Intelligent Transportation Systems, 23 (9), 15546–15557.
   Web of Science ®Google Scholar
• Liu, Yahui, et al., 2019. Deepcrack: a deep hierarchical feature learning architecture for crack segmentation. Neurocomputing, 338, 139–153.
   Web of Science ®Google Scholar
• Long, J., Shelhamer, E., and Darrell, T., 2015. Fully convolutional networks for semantic segmentationed. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 3431–3440.
   Google Scholar
• Massahi, A., et al., 2018. Investigation of pavement raveling performance using smartphone. International Journal of Pavement Research and Technology, 11 (6), 553–563.
   Google Scholar
• Oloufa, A., Mahgoub, H., and Ali, H., 2004. Infrared Thermography for Asphalt Crack Imaging and Automated Detection. Transportation Research Record: Journal of the Transportation Research Board, 1889 (1), 126–133.
   Google Scholar
• Ronneberger, O., Fischer, P., and Brox, T., 2015. U-net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention, MICCAI 2015, 234–241.
   Google Scholar
• Safaei, N., et al., 2021. An automatic image processing algorithm based on crack pixel density for pavement crack detection and classification. International Journal of Pavement Research and Technology, 15 (1), 159–172.
   Google Scholar
• Saining Xie, R.G., et al., 2017. Aggregated residual transformations for deep neural networks. In: 30th IEEE/CVF conference on computer vision and pattern recognition (CVPR), 5987–5995.
   Google Scholar
• Shi, Yong, et al., 2016. Automatic road crack detection using random structured forests. IEEE Transactions on Intelligent Transportation Systems, 17, 3434–3445.
   Web of Science ®Google Scholar
• Simonyan, K., and Zisserman, A.J.a.E.P., 2014. Very deep convolutional networks for large-scale image recognition. arXiv.
   Google Scholar
• Szegedy, C.L., et al., 2015. Going deeper with convolutionsed. In: 2015 conference on computer vision and pattern recognition (CVPR), 1–9.
   Google Scholar
• Szegedy, C., et al., 2016. Rethinking the inception architecture for computer visioned. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), 2818–2826.
   Google Scholar
• Szegedy, C.I., et al., 2017. Inception-v4, inception-resnet and the impact of residual connections on learninged. In: 31st AAAI Conference on Artificial Intelligence, 4278–4284.
   Google Scholar
• Yang, X., et al., 2018. Automatic pixel-level crack detection and measurement using fully convolutional network. Computer-Aided Civil and Infrastructure Engineering, 33 (12), 1090–1109.
   Web of Science ®Google Scholar
• Yang, F., et al., 2020. Feature pyramid and hierarchical boosting network for pavement crack detection. IEEE Transactions on Intelligent Transportation Systems, 21 (4), 1525–1535.
   Web of Science ®Google Scholar
• Zhang, L., et al., 2016. Road crack detection using deep convolutional neural network. IEEE International Conference on Image Processing (ICIP), 3708–3712.
   Google Scholar
• Zhang, A., et al., 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.
   Web of Science ®Google Scholar
• Zhang, Y., and 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.
   Web of Science ®Google Scholar
• Zhou, Z., et al., 2020. Unet plus plus redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transactions on Medical Imaging, 39 (6), 1856–1867.
   PubMed Web of Science ®Google Scholar
• Zou, Qin, et al., 2012. Cracktree: automatic crack detection from pavement images. Pattern Recognition Letters, 33, 227–238.
   Web of Science ®Google Scholar
• Zou, Q., et al., 2018. Deepcrack: learning hierarchical convolutional features for crack detection. IEEE Transactions on Image Processing, 28 (3), 1498–1512.
   Web of Science ®Google Scholar