[1] 程姝菲,黄宏伟.盾构隧道长期渗漏水检测新方法[J].地下空间与工程学报,2014,10(3):733-738. [2] 雪彦鹏,何杰,高斌,等.运营期隧道渗漏水病害无损检测及处治措施研究[J].重庆建筑,2017,16(10):33-37. [3] 许献磊,马正,李俊鹏,等.地铁隧道管片背后脱空及渗水病害检测方法[J].铁道建筑,2019,59(7):51-56. [4] 吴杭彬,于鹏飞,刘春,等.基于红外热成像的地铁隧道渗漏水提取[J].工程勘察,2019,47(2):44-49. [5] 王烽人.隧道渗漏红外特征识别与提取技术研究[D].武汉:华中科技大学,2018. [6] 顾天雄,朱福龙,程国开,等.隧道衬砌渗漏水红外特征模拟试验及图像处理[J].武汉工程大学学报,2017,39(1):96-102. [7] 豆海涛,黄宏伟,薛亚东.隧道衬砌渗漏水红外辐射特征影响因素试验研究[J].岩石力学与工程学报,2011,30(12):2426-2434. [8] HUANG Hongwei,LI Qingtong,ZHANG Dongming.Deep learning based image recognition for crack and leakage defects of metro shield tunnel[J].Tunnelling and Underground Space Technology,2018,77:166-176. [9] ZHAO Shuai,ZHANG Dongming,HUANG Hongwei.Deep learning-based image instance segmentation for moisture marks of shield tunnel lining[J].Tunnelling and Underground Space Technology,2020,95:103156. [10] REN Yupeng,HUANG Jisheng,HONG Zhiyou,et al.Image-based concrete crack detection in tunnels using deep fully convolutional networks[J].Construction and Building Materials,2020,234:117367. [11] XIONG Leijin,ZHANG Dingli,ZHANG Yu.Water leakage image recognition of shield tunnel via learning deep feature representation[J].Journal of Visual Communication and Image Representation,2020,71:102708. [12] 高新闻,简明,李帅青.基于FCN与视场柱面投影的隧道渗漏水面积检测[J].计算机测量与控制,2019,27(8):44-48. [13] KAASALAINEN S,VAIN A,KROOKS A,et al.Topographic and distance effects in laser scanner intensity correction[J].Engineering,Environmental Science,Physics,2009:2309993. [14] TAN Kai,CHENG Xiaojun.Correction of incidence angle and distance effects on TLS intensity data based on reference targets[J].Remote Sensing,2016,8(3):251. [15] TAN Kai,CHENG Xiaojun.Intensity data correction based on incidence angle and distance for terrestrial laser scanner[J].Journal of Applied Remote Sensing,2015,9:094094. [16] 彭斌,祝志恒,阳军生,等.基于全景展开图像的隧道衬砌渗漏水数字化识别方法研究[J].现代隧道技术,2019,56(3):31-37. |