多特征模板匹配的多源盾构隧道螺栓孔精准识别  

Precise determination of bolt holes in multiple source shield tunnels by multi-feature template matching

作　　者：王少宁 杨元维 许磊 顾世成[3] 高贤君[1] 尹正浩 钟康 刘振宇 WANG Shaoning;YANG Yuanwei;XU Lei;GU Shicheng;GAO Xianjun;YIN Zhenghao;ZHONG Kang;LIU Zhenyu(School of Geosciences,Yangtze University,Wuhan 430100,China;China Railway Design Corporation,Tianjin 300251,China;No.1 Middle School Affiliated to Central China Normal University,Wuhan 430223,China)

机构地区：[1]长江大学地球科学学院,湖北武汉430100 [2]中国铁路设计集团有限公司,天津300251 [3]华中师范大学第一附属中学,湖北武汉430223

出　　处：《测绘通报》2025年第3期59-65,共7页Bulletin of Surveying and Mapping

基　　金：城市轨道交通数字化建设与测评技术国家工程实验室开放课题(2023ZH01);湖北省教育厅科学研究计划重点项目(D20231304);西藏自治区科技计划重大专项(XZ202402ZD0001);自然资源部环鄱阳湖区域矿山环境监测与治理重点实验室开放基金(MEMI-2021-2022-08);天津市科技计划(23YFYSHZ00190,23YFZCSN00280);湖南省自然科学基金项目部门联合基金(2024JJ8327);江西省自然科学基金(20232ACB204032)。

摘　　要：螺栓孔作为盾构隧道的关键力学结构,对隧道整体结构的稳定性至关重要。由螺栓失效引发的渗水和裂缝等病害现象,对隧道的安全运营构成潜在威胁。为消除检测人员的安全隐患并提高螺栓孔的检测效率,本文提出了一种基于多特征模板匹配的多源盾构隧道螺栓孔精准识别方法。首先,将隧道断面点云的几何中心作为视点,将隧道点云进行圆柱投影展开成2.5维点云;其次,通过DBSCAN聚类方法提取螺栓孔点云并采用欧式聚类生成其中心坐标集;然后,以扫描测线为单元对2.5维点云及螺栓孔中心坐标集进行逐测线投影,将三维点云信息融入二维图像中以此锁定目标检测区域;最后,通过多特征模板匹配的方法对螺栓孔进行精准识别。本文设计的基于点云形状特征的DBSCAN聚类方法能够提取点云数据中所有的螺栓孔,同时识别率和平均相似度分别能够达到98.76%和98.79%,且在耗时相近的情况下,平均偏差更小,表现出更好的稳健性。本文充分考虑螺栓孔点云的形状特征,实现了螺栓孔点云的精准分类,并通过融合盾构隧道的三维与二维数据进一步提高了盾构隧道图像上目标识别的精度。Given that the bolt hole is the primary mechanical component of the shield tunnel,its overall structural stability depends heavily on it.A possible risk to the secure functioning of tunnels is disease phenomena such water seepage and cracks brought on by bolt failure.In order to eliminate the potential safety hazards of inspectors and improve the detection efficiency of bolt holes,this paper proposes an accurate identification method for bolt holes in multi-source shield tunnels based on multi-feature template matching.Firstly,the geometric center of the tunnel section point cloud is chosen as the viewpoint,and the tunnel point cloud is expanded into a 2.5 D point cloud using cylindrical projection.The bolt hole point cloud is then extracted using DBSCAN,and its center coordinate set is generated using Euclidean clustering.The DBSCAN clustering method based on the shape features of the point cloud designed in this paper can extract all bolt holes from the point cloud data.The average similarity and recognition rate of the proposed method can reach 98.79%and 98.76%,respectively,and the average deviation is smaller and more robust in the case of similar time.In this paper,the shape characteristics of the bolt hole point cloud are fully considered to realize the accurate classification of the bolt hole point cloud,and the accuracy of target recognition on the shield tunnel image is further improved by fusing the three-dimensional and two-dimensional data of the shield tunnel.

关 键 词：盾构隧道 螺栓孔 跨模态 模板匹配 目标识别 

分 类 号：P237[天文地球—摄影测量与遥感]