3D Imaging based pavement texture evaluation

References

• Albert, TA, and Slaaf, DW, 1995. A rapid regional filling technique for complex binary images. Computer & Graphics, 19 (4), 541–549.
   Web of Science ®Google Scholar
• ASTM. 2015. ASTM E1845 – 15 Standard Practice for Calculating Pavement Macrotexture Mean Profile Depth, 2015.
   Google Scholar
• ASTM E2157-15: Standard Test Method for Measuring Pavement Macrotexture Properties Using the Circular Track Meter, ASTM International, West Conshohocken, PA, 2019.
   Google Scholar
• Cui, X Z, Zhou, X L, Lou, J J, et al., 2017. Measurement method of asphalt pavement mean texture depth based on multi-line laser and binocular vision[J]. International Journal of Pavement Engineering, 18 (5), 459–471.
   Web of Science ®Google Scholar
• Ding, Shihai, et al., 2020. MTD measurement of asphalt pavement based on high precision laser section elevation [J]. Journal of Southeast University Natural Science Edition, 50 (1), 137–142.
   Google Scholar
• Feng, L., and Soon, SH., 1998. An effective 3D seed fill algorithm [J]. Computer & Graphics, 22 (5), 641–644.
   Web of Science ®Google Scholar
• Feng, Xingle, Zhang, Haiou, Li, Wei, et al., 2018. MTD measured method of asphalt pavement based on static and dynamic anti-sliding characteristics [J/OL]. Journal of Southwest Jiaotong University. http://www.doc88.com/p-2778402625252.html.
   Google Scholar
• Gendy, A. E., et al., 2011. Stereo-vision applications to reconstruct the 3D texture of pavement surface [J]. International Journal of Pavement Engineering, 12 (3), 263–273.
   Web of Science ®Google Scholar
• Geraets, WG, Van Daatselaar, AN, and Verheij, JG, 2004. An efficient filling algorithm for counting regions. Computer Methods and Programs in Biomedicine, 76, 1–11.
   PubMed Web of Science ®Google Scholar
• Hao, Shi, 2020. Investigation on seepage behavior in asphalt mixture based on micro-scale Air void morphology genic library [D]. Haibin: Harbin Institute of Technology. (in Chinese).
   Google Scholar
• He, Yixuan, Hu, Tianyi, and Zeng, Delu. 2019. Scan-flood fill (SCAFF): an efficient automatic precise region filling algorithm for complicated regions [C]. 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, 16(20), 761-769.
   Google Scholar
• Jou, SB., and Tsai, MD., 2003. A fast 3D seed-filling algorithm [J]. Visual Computer, 19 (4), 243–251.
   Web of Science ®Google Scholar
• JTG E60: Field Test Methods of Subgrade and Pavement for Highway Engineering (2008).
   Google Scholar
• Khayal, M S H, Khan, A, Bashir, S, et al., 2011. Modified new algorithm for seed filling [J]. Journal of Theoretical and Applied Information Technology, 11 (1), 28–32.
   Google Scholar
• Kogbara, Reginald B., et al., 2016. A state-of-the-art review of parameters influencing measurement and modeling of skid resistance of asphalt pavements [J]. Construction and Building Materials, 114, 602–617.
   Web of Science ®Google Scholar
• Le Jun, S, and Hao, Z, 1995. A new contour fill algorithm for outlined character image generation. Computer & Graph, 19, 551–556.
   Web of Science ®Google Scholar
• Li, Pengfei, and Souleyrette, R.R., 2016. A generic approach to estimate freeway traffic time using vehicle ID-matching technologies [J]. Computer-aided Civil and Infrastructure Engineering, 31 (5), 351–365.
   Web of Science ®Google Scholar
• Li, L., Wang, K.C.P., and Li, Q.J., 2016. Geometric texture indicators for safety on AC pavements with 1 mm 3D Laser Texture data. International Journal of Pavement Research and Technology, 9 (1), 49–62. doi:10.1016/j.ijprt.2016.01.004.
   Google Scholar
• Mccullough, B F, and Hankins, K D, 1966. Skid resistance guidelines for surface improvements on texas highways [R]. Austin: The Texas Highway Department.
   Google Scholar
• Michigan Metrology Surface Texture Parameters Glossary. Michigan Metrology, LLC, Livonia, Mich., 2014.
   Google Scholar
• Praticò, F. G., and Vaiana, R, 2015. A study on the relationship between mean texture depth and mean profile depth of asphalt pavements [J]. Construction and Building Materials, 101, 72–79.
   Web of Science ®Google Scholar
• Puzzo, L., Loprencipe, G., and Tozzo, C., 2017. Three-dimensional survey method of pavement texture using photographic equipment [J]. Measurement, 111, 146–157.
   Web of Science ®Google Scholar
• Report of Technical Committee on Surface Characteristics. 1991. Proceeding of Xth World Road Congress, Mrrakesh, 1991, 22-28.
   Google Scholar
• Vacura, P., Lane, L., and Cheng, D. 2013. Caltrans Using Fog and Rejuvenating Seals on State Highways for Preventative Maintenance Newsletter of the California Pavement Preservation Center No. 28, December, 2013, pp. 4-7.
   Google Scholar
• Wang, W. F., et al., 2010. Feature description and its solution of asphalt road pavement micro-topography based on fractal theory [J]. Journal of Jilin University (Engineering and Technology Edition, 40 (6), 38–42.
   Google Scholar
• Wen, Jing, 2009. Study on evaluating texture depth of asphalt pavement with digital technology [D]. Xi’an: Changan University. (in Chinese).
   Google Scholar
• Wu, Qian, Ju, Tai-liang, and Yao, Bo-wei, 2001. Using seed-fill algotithm to Get shape character of image [J]. Telecommunication Engineering, 4, 88–90.
   Google Scholar
• Xiaoming, H., and Binshuang, Z, 2019. Research status and progress for skid resistance performance of asphalt pavement [J]. China Journal of Highway and Transport.
   Google Scholar
• Yalei, Zhang, Jian, Wang, Houzeng, Han, et al., 2020. Research and precision analysis of indoor ranging model based on bluetooth [J]. Science of Surveying and Mapping.
   Google Scholar
• Yang, G W, Li, Q J, Zhan, Y, et al., 2018. Convolutional neural network–based friction model using pavement texture data [J]. Journal of Computing in Civil Engineering, 32 (6), 04018052.
   Web of Science ®Google Scholar
• Yu, Wei-wei, He, Fei, and Xi, Ping, 2010. A rapid 3D seed-filling algorithm based on scan slice [J]. Computer & Graphics, 34 (4), 449–459.
   Web of Science ®Google Scholar
• Zheng, T, et al., 2018. Convolutional neural network for asphalt pavement surface texture analysis [J]. Computer-Aided Civil and Infrastructure Engineering, 33 (12), 1056–1072.
   Web of Science ®Google Scholar
• Zhou, X L, Jiang, N D, Xiao, W X, et al., 2014. Measurement method for mean texture depth of asphalt pavement based on laser vision [J]. China Journal of Highway and Transport, 27 (3), 11–16.
   Google Scholar