A global context and pyramidal scale guided convolutional neural network for pavement crack detection

References

• Abraham, N., and Khan, N.M., 2019, April. A novel focal Tversky loss function with improved attention u-net for lesion segmentation. In 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019). IEEE, 683–687.
   Google Scholar
• Ahmadi, A., Khalesi, S., and Golroo, A., 2022. An integrated machine learning model for automatic road crack detection and classification in urban areas. International Journal of Pavement Engineering, 23 (10), 3536–3552.
   Web of Science ®Google Scholar
• Akagic, A., et al., 2018, May. Pavement crack detection using Otsu thresholding for image segmentation. In 2018 41st international convention on information and communication technology, electronics and microelectronics (MIPRO). IEEE, 1092–1097.
   Google Scholar
• Ayenu-Prah, A., and Attoh-Okine, N., 2008. Evaluating pavement cracks with bidimensional empirical mode decomposition. EURASIP Journal on Advances in Signal Processing, 2008, 1–7.
   Google Scholar
• Berman, M., Triki, A.R., and Blaschko, M.B., 2018. The lovász-softmax loss: a tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4413–4421.
   Google Scholar
• Cha, Y.J., and Wang, Z., 2018. Unsupervised novelty detection–based structural damage localization using a density peaks-based fast clustering algorithm. Structural Health Monitoring, 17 (2), 313–324. doi:10.1177/1475921717691260.
   Web of Science ®Google Scholar
• Chaurasia, A., and Culurciello, E., 2017, December. Linknet: exploiting encoder representations for efficient semantic segmentation. In 2017 IEEE visual communications and image processing (VCIP). IEEE, 1–4.
   Google Scholar
• Chen, C., et al., 2022. Pavement crack detection and classification based on fusion feature of LBP and PCA with SVM. International Journal of Pavement Engineering, 23 (9), 3274–3283.
   Web of Science ®Google Scholar
• Cheng, J., et al., 2018, October. Pixel-level crack detection using U-net. In TENCON 2018-2018 IEEE Region 10 Conference. IEEE, 0462–0466.
   Google Scholar
• 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. doi:10.1111/j.1467-8667.2011.00736.x.
   Web of Science ®Google Scholar
• Cubero-Fernandez, A., et al., 2017. Efficient pavement crack detection and classification. EURASIP Journal on Image and Video Processing, 2017 (1), 1–11. doi:10.1186/s13640-017-0187-0.
   Google Scholar
• Dong, Z., et al., 2020. Patch-based weakly supervised semantic segmentation network for crack detection. Construction and Building Materials, 258, 120291. doi:10.1016/j.conbuildmat.2020.120291.
   Web of Science ®Google Scholar
• Duta, I.C., et al., 2020. Pyramidal convolution: rethinking convolutional neural networks for visual recognition. arXiv preprint arXiv:2006.11538.
   Google Scholar
• Eisenbach, M., et al., 2017, May. How to get pavement distress detection ready for deep learning? A systematic approach. In 2017 international joint conference on neural networks (IJCNN). IEEE, 2039–2047.
   Google Scholar
• Fan, Z., et al., 2020. Ensemble of deep convolutional neural networks for automatic pavement crack detection and measurement. Coatings, 10 (2), 152. doi:10.3390/coatings10020152.
   Web of Science ®Google Scholar
• He, K., et al., 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 770–778.
   Google Scholar
• He, K., et al., 2017. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, 2961–2969.
   Google Scholar
• Hoang, N.D., and Nguyen, Q.L., 2019. A novel method for asphalt pavement crack classification based on image processing and machine learning. Engineering with Computers, 35 (2), 487–498. doi:10.1007/s00366-018-0611-9.
   Web of Science ®Google Scholar
• Hu, Jie, Shen, Li, and Sun, Gang, 2018. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, 7132–7141.
   Google Scholar
• Hu, Y., Zhao, C.X., and Wang, H.N., 2010. Automatic pavement crack detection using texture and shape descriptors. IETE Technical Review, 27 (5), 398–405. doi:10.4103/0256-4602.62225.
   Web of Science ®Google Scholar
• Huang, G., et al., 2017. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, 4700–4708.
   Google Scholar
• Jenkins, M.D., et al., 2018, September. A deep convolutional neural network for semantic pixel-wise segmentation of road and pavement surface cracks. In 2018 26th European signal processing conference (EUSIPCO). IEEE, 2120–2124.
   Google Scholar
• Jing, L., and Aiqin, Z., 2010, April. Pavement crack distress detection based on image analysis. In 2010 international conference on machine vision and human-machine interface. IEEE, 576–579.
   Google Scholar
• Krizhevsky, A., Sutskever, I., and Hinton, G.E., 2012. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105.
   Google Scholar
• Lau, S.L., et al., 2020. Automated pavement crack segmentation using u-net-based convolutional neural network. IEEE Access, 8, 114892–114899. doi:10.1109/ACCESS.2020.3003638.
   Google Scholar
• Li, W., et al., 2017. Three-dimensional pavement crack detection algorithm based on two-dimensional empirical mode decomposition. Journal of Transportation Engineering, Part B: Pavements, 143 (2), 04017005. doi:10.1061/JPEODX.0000006.
   Web of Science ®Google Scholar
• Li, Y., et al., 2019. YOLOv3-lite: a lightweight crack detection network for aircraft structure based on depthwise separable convolutions. Applied Sciences, 9 (18), 3781. doi:10.3390/app9183781.
   Google Scholar
• Li, Q., and Liu, X., 2008, May. Novel approach to pavement image segmentation based on neighboring difference histogram method. In 2008 congress on image and signal processing. IEEE, 2, 792–796.
   Google Scholar
• Liu, W., et al., 2019a. FPCNet: fast pavement crack detection network based on encoder-decoder architecture. arXiv preprint arXiv:1907.02248.
   Google Scholar
• Liu, Y., et al., 2019b. Deepcrack: a deep hierarchical feature learning architecture for crack segmentation. Neurocomputing, 338, 139–153. doi:10.1016/j.neucom.2019.01.036.
   Web of Science ®Google Scholar
• Liu, Z., et al., 2019c, October. A two-stage approach for automated prostate lesion detection and classification with mask R-CNN and weakly supervised deep neural network. In: D. Nguyen, L. Xing, and S. Jiang, eds. Workshop on artificial intelligence in radiation therapy. (pp. 43–51). Cham: Springer.
   Google Scholar
• Ma, N., et al., 2018. Shufflenet v2: practical guidelines for efficient CNN architecture design. In Proceedings of the European conference on computer vision (ECCV), 116–131.
   Google Scholar
• Mei, Q., Gül, M., and Azim, M.R., 2020. Densely connected deep neural network considering connectivity of pixels for automatic crack detection. Automation in Construction, 110, 103018. doi:10.1016/j.autcon.2019.103018.
   Web of Science ®Google Scholar
• Nguyen, T.S., et al., 2011, September. Free-form anisotropy: a new method for crack detection on pavement surface images. In 2011 18th IEEE international conference on image processing. IEEE, 1069–1072.
   Google Scholar
• Nguyen, N.T.H., et al., 2018, December. Pavement crack detection using convolutional neural network. In Proceedings of the Ninth international symposium on information and communication technology, 251–256.
   Google Scholar
• Nisanth, A., and Mathew, A., 2014. Automated visual inspection on pavement crack detection and characterization. International Journal of Technology and Engineering System, 6 (1), 14–20.
   Google Scholar
• Nishikawa, T., et al., 2012. Concrete crack detection by multiple sequential image filtering. Computer-Aided Civil and Infrastructure Engineering, 27 (1), 29–47. doi:10.1111/j.1467-8667.2011.00716.x.
   Web of Science ®Google Scholar
• Oliveira, H., and Correia, P.L., 2014, October. CrackIT—An image processing toolbox for crack detection and characterization. In 2014 IEEE international conference on image processing (ICIP). IEEE, 798–802.
   Google Scholar
• Ren, S., et al., 2015. Faster r-cnn: towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, 28.
   Google Scholar
• Salman, M., et al., 2013, October. Pavement crack detection using the Gabor filter. In 16th international IEEE conference on intelligent transportation systems (ITSC 2013). IEEE, 2039–2044.
   Google Scholar
• Shi, Y., et al., 2016. Automatic road crack detection using random structured forests. IEEE Transactions on Intelligent Transportation Systems, 17 (12), 3434–3445. doi:10.1109/TITS.2016.2552248.
   Web of Science ®Google Scholar
• Subirats, P., et al., 2006, October. Automation of pavement surface crack detection using the continuous wavelet transform. In 2006 international conference on image processing. IEEE, 3037–3040.
   Google Scholar
• Tong, Z., et al., 2018. Recognition of asphalt pavement crack length using deep convolutional neural networks. Road Materials and Pavement Design, 19 (6), 1334–1349. doi:10.1080/14680629.2017.1308265.
   Web of Science ®Google Scholar
• Tran, T.S., et al., 2022. A two-step sequential automated crack detection and severity classification process for asphalt pavements. International Journal of Pavement Engineering, 23 (6), 2019–2033.
   Web of Science ®Google Scholar
• Wang, M., and Cheng, J.C., 2018, June. Development and improvement of deep learning based automated defect detection for sewer pipe inspection using faster R-CNN. In: I.F.C. Smith and B. Domer, eds. In workshop of the European group for intelligent computing in engineering. Cham: Springer; 171–192.
   Google Scholar
• Wang, W., and Su, C., 2020. Convolutional neural network-based pavement crack segmentation using pyramid attention network. IEEE Access, 8, 206548–206558. doi:10.1109/ACCESS.2020.3037667.
   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. doi:10.1111/mice.12412.
   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. doi:10.1109/TITS.2019.2910595.
   Web of Science ®Google Scholar
• Zhang, L., et al., 2016, September. Road crack detection using deep convolutional neural network. In 2016 IEEE international conference on image processing (ICIP). IEEE, 3708–3712.
   Google Scholar
• Zhao, H., et al., 2017. Pyramid scene parsing network. In proceedings of the IEEE conference on computer vision and pattern recognition, 2881–2890.
   Google Scholar
• Zou, Q., et al., 2012. Cracktree: automatic crack detection from pavement images. Pattern Recognition Letters, 33 (3), 227–238. doi:10.1016/j.patrec.2011.11.004.
   Web of Science ®Google Scholar