摘要
渗漏水是盾构隧道结构存在潜在损伤或缺陷的重要表征,快速、准确检测出渗漏水位置,对隧道安全运营和维护具有重要意义。现有的方法大多采用光学影像对隧道渗漏水进行检测,受隧道内空间和光线条件限制,难以获得高质量病害图片。因此,本文提出了一种基于激光点云数据与改进Mask RCNN相结合的渗漏水检测方法。首先对激光点云反射强度进行修正;然后生成灰度图像并建立渗漏水病害数据集;最后在Mask RCNN算法中引入空洞卷积和变形卷积,实现了隧道渗漏水病害的快速检测。利用某地铁采集的数据进行验证,结果表明,本文提出的改进Mask RCNN算法相较于原始算法和FCN算法检测精度均有明显提升,在盾构隧道渗漏水识别方面性能表现较好。
Water leakage is an important characterization of the potential damage or defects of the shield tunnel structure.Rapid and accurate detection of the tunnel leakage site is of great significance to the safe operation and maintenance of the tunnel.However,most of the existing methods use optical images to detect the tunnel water leakage,but due to the tunnel space limitations and light conditions,it is difficult to obtain high-quality disease pictures.In this regard,a water leakage detection method based on terrestrial laser scanning point cloud and improved Mask RCNN is proposed.Firstly,the laser point cloud reflection intensity is corrected,and then the gray scale image is generated and the water leakage disease data set is established.Finally,the atrous convolution and deformation convolution are introduced in the Mask RCNN algorithm to realize the rapid detection of tunnel water leakage disease.The data collected in metro are used for verification.Experimental results show that,compared to the original algorithm and FCN algorithm,the detection accuracy of the proposed improved Mask RCNN algorithm is significantly improved,and it has a good performance in water leakage identification in shield tunnel.
作者
王健
郑理科
吴斌杰
齐智宇
WANG Jian;ZHENG Like;WU Binjie;QI Zhiyu(College of Geodesy and Geomatics,Shandong University of Science and Technology,Qingdao 266590,China;Qingdao Key Laboratory of Beidou Navigation and Intelligent Spatial Information Technology Application,Qingdao 266590,China;Xilingol League Shandong Gold Alhada Mining Co.,Ltd.,Xilingol 026300,China)
出处
《测绘通报》
CSCD
北大核心
2024年第2期170-177,共8页
Bulletin of Surveying and Mapping
基金
山东省自然科学基金(ZR2023MD027)
国家重点研发计划(2022YFB3903501)。
关键词
盾构隧道
点云
反射强度修正
Mask
RCNN
渗漏水检测
shield tunnel
point cloud
reflection intensity correction
Mask RCNN
water leakage detection
