TBM掘进岩渣图像分割与识别方法研究  

Segmentation and Recognition Method of Rock Muck Image during Tunnel Boring Machine Construction

作　　者：李青蔚 杜立杰[1] 杨亚磊 刘雷涛 蔡龙 刘金辉 LI Qingwei;DU Lijie;YANG Yalei;LIU Leitao;CAI Long;LIU Jinhui(Shijiazhuang Tiedao University,Shijiazhuang 050043,Hebei,China;China Railway 19 Bureau Group First Co.,Ltd.,Liaoyang 111000,Liaoning,China)

机构地区：[1]石家庄铁道大学,河北石家庄050043 [2]中铁十九局集团第一工程有限公司,辽宁辽阳111000

出　　处：《隧道建设（中英文）》2021年第5期803-813,共11页Tunnel Construction

基　　金：新疆EH工程科技计划(EQ075/FY056);国家973计划(2014CB046906);国家铁路集团科技计划(2016G004-A,P2018G007)。

摘　　要：为促进TBM在掘岩体的智能化识别及安全高效施工,依托朱溪水库引水隧洞等TBM施工工程,现场采集岩渣图像,首先,总结分析岩渣特征与岩体完整程度的对应关系;然后,根据TBM岩渣图像特点,提出基于改进标记分水岭的岩渣图像分割算法,对岩渣图像进行特征提取与分类识别;最后,对现场采集的不同岩体完整程度下的160张岩渣图像进行工程应用,验证方法的准确性。结果表明:A、B、C类岩体对应的岩渣图像分类结果准确率分别为96.3%、94%、86.7%。该方法可以有效实现TBM掘进岩渣的自动分析与识别,可为不良地质TBM安全施工预警提供保障。Real-time judgment and early warnings of tunnel face surrounding rock conditions during tunnel boring machine(TBM)tunneling are paramount in intelligent rock mass identification and safe and efficient TBM tunneling.A few onsite images of rock muck are collected from some TBM projects,i.e.Zhuxi Reservoir diversion tunnel.First,the correspondence between rock muck and rock mass integrity is summarized.Second,according to the characteristics of rock muck images,a new segmentation algorithm based on the improved marked watershed transform is proposed to extract and classify the muck images.Finally,the accuracy of the method is verified using 160 pieces of onsite rock muck images under different degrees of rock mass integrity.The results show that the average accuracies of A,B,and C image classification are 96.3%,94%,and 86.7%,respectively,indicating that the method can effectively realize automatic analysis and recognition of rock muck,providing an early warning for TBM tunneling in adverse geology.

关 键 词：TBM 标记分水岭变换 岩渣 图像分割 图像识别 

分 类 号：U45[建筑科学—桥梁与隧道工程]