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Outdated or sketchy inventory of street furniture may misguide the planners on the renovation and upgrade of transportation infrastructures, thus posing potential threats to traffic safety. Previous studies have taken their steps using point clouds or street-view imagery (SVI) for street furniture inventory, but there remains a gap to balance semantic richness, localization accuracy and working efficiency. Therefore, this paper proposes an effective pipeline that combines SVI and point clouds for the inventory of street furniture. The proposed pipeline encompasses three steps: (1) Off-the-shelf street furniture detection models are applied on SVI for generating two-dimensional (2D) proposals and then three-dimensional (3D) point cloud frustums are accordingly cropped; (2) The instance mask and the instance 3D bounding box are predicted for each frustum using a multi-task neural network; (3) Frustums from adjacent perspectives are associated and fused via multi-object tracking, after which the object-centric instance segmentation outputs the final street furniture with 3D locations and semantic labels. This pipeline was validated on datasets collected in Shanghai and Wuhan, producing component-level street furniture inventory of nine classes. The instance-level mean recall and precision reach 86.4%, 80.9% and 83.2%, 87.8% respectively in Shanghai and Wuhan, and the point-level mean recall, precision, weighted coverage all exceed 73.7%. [ABSTRACT FROM AUTHOR]

Shield subway tunnels are critical urban infrastructure and precise, rapid deformation detection method is required for the maintenance of these tunnels. A tunnel deformation detection system called Railway Mobile Measurement System (RMMS) was developed based on mobile laser scanning technique. Herein, detailed analysis is given to prove that profiles from the laser scanner can be seen as cross-sections of the tunnel and that the corresponding error can be ignored. A wavelet filtering algorithm was developed to filter the point cloud of tunnel ancillary facilities in the cross-section points. A subsequent ellipse fitting and deformation detection method is proposed. A cross-section location method for subway tunnels is also presented based on the greyscale image converted from the tunnel's point cloud. A tunnel ring segments seams detection method is proposed to detect tunnel ring segments dislocation. The accuracy of the system was verified in a subway tunnel in Wuhan, China with a total station. The accuracy of cross-section measurement was 1.5 mm, the repeatability of the overall and local deformation measurement was 0.5 and 0.9 mm, respectively. This system completely fulfils the accuracy requirement of railway tunnels deformation detection and its efficiency is significantly higher than that of the traditional method. • The error of directly obtained laser scanning cross-section points of subway tunnels is analyzed in detail. • A wavelet filtering algorithm is proposed to filter point cloud of tunnel ancillary facilities. • Tunnel's overall and local deformation is detected based on ellipse fitting of cross-section points. • A tunnel ring segments dislocation detection method based on template matching and segments seams detection is proposed. [ABSTRACT FROM AUTHOR]