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Yang, \u201cPavement crack segmentation technology based on improved fully convolutional networks,\u201d Computer Engineering and Applications, vol.55, no.16, pp.235-239, 2019. 10.3778\/j.issn.1002-8331.1901-0068"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] A. Zhang, K.C.P. Wang, Y. Fei, Y. Liu, C. Chen, G. Yang, J.Q. Li, E. Yang, and S. Qiu, \u201cAutomated pixel-level pavement crack detection on 3D asphalt surfaces with a recurrent neural network,\u201d Computer Aid Civ Infrastructure Engineering, vol.34, no.3, pp.213-229, 2019. 10.1111\/mice.12409","DOI":"10.1111\/mice.12409"},{"key":"9","unstructured":"[9] Suya, Du Jianchao. S. Zhu, J. Du, Y. Li, and X. Wang, \u201cMethod for bridge crack detection based on the U-Net convolutional networks,\u201d Journal of Xidian University, vol.46, no.4, pp.35-42, 2019. 10.19665\/j.issn1001-2400.2019.04.006"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] P. Pan, Y. Xu, C. Xing, and Y. 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