The manual method of shield segment joint identification has been gradually superseded with a point cloud-based data processing technology.Nevertheless,there will be issues with incorrect or incapable identification brought on by the obstruction of auxiliary facilities when the traditional point cloud processing technique extracts bolt holes and seams,which will lower the accuracy of target recognition.To address this issue,a cross-modal feature fusion template-driven hole and joint identification approach for shield tunnels was presented,using as an example the data of a metro tunnel segment that opened in Guangzhou in 2003.Firstly,using the scanning survey line as the unit,a two-dimensional image of the tunnel was created by projecting the geometric center of the point cloud of the tunnel section as the viewpoint.The two-dimensional image was then segmented,the longitudinal seam was found using Canny edge detection and a Hough transform buffer,the shield ring classification was achieved using double template matching,and the transverse seam and bolt hole coarse positioning was done in accordance with the ring template.In order to achieve the intelligent identification of segment holes and seams in shield tunnels,the ring template was finally precisely corrected using the central coordinates of the bolt hole point cloud DBSCAN clustering.The results are drawn as follows.The proposed method can accurately identify holes and seams in the case of ancillary facilities occlusion interference.Accurate classification of shield rings can be achieved by the dual template-driven designed classification method that considers local morphological features.By taking into account the spatial position relationship of shield segments,the suggested hole and crack identification technique may effectively increase the accuracy of hole and crack identification.The proposed approach outperforms comparable methods in terms of accuracy and resilience,with less average variation,higher recognition accuracy,and better recognition rates and times.By combining the data features from the 3D point cloud and the 2D point cloud projection images of the shield tunnel,the method can accurately classify the shield ring by using a double template,account for local morphological features,and enhance the accuracy of hole and crack recognition based on the spatial position relationship of the shield segment.The results can serve as a guide for future automated and precise target information identification of bolt holes and shield tunnel seams.
关键词
盾构隧道孔缝识别/特征融合/模板驱动/跨模态/移动激光扫描点云
Key words
identification of shield tunneling bolt hole and seam/feature fusion/template-driven/cross-modal/mobile laser scanning point cloud
维普
万方数据