Affiliation:
1. BIM Cluster, Department of Future & Smart Construction Research, Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Republic of Korea
2. GIS Research Center, Geospatial Information Technology, Sungnam-si 13487, Republic of Korea
Abstract
Digital twin technology can be used for disaster safety management of underground utility tunnels, which are social infrastructures. To verify its efficiency in an underground utility tunnel, it must be implemented and monitored in the real world. Therefore, the state of information about an object and the position of an object in the real world, which can change upon movement, should be correspondingly reflected in the digital model. Underground utility tunnels are monitored through closed circuit television (CCTV) cameras because the main infrastructure facilities are installed in a long and narrow form, which limits access to them. Therefore, synchronization of a digital twin model of an underground utility tunnel requires a method for identifying the location of an object through an installed CCTV camera. This study compares the DenseDepth transfer learning method and the coordinate system conversion method through floor plan projection to estimate depth using a single CCTV camera installed in an underground utility tunnel. The coordinate system conversion method through floor plane projection showed an error of 0.23 m to 1.5 m within a 50 m range. The DenseDepth transfer learning method showed an error of 1 m to 3 m; therefore, it was considered unsuitable for distance estimation. The coordinate system conversion method through floor plane projection showed a smaller error even at a longer distance than the DenseDepth transfer learning method. Therefore, the coordinate system conversion method through floor plane projection was considered more suitable than the DenseDepth transfer learning method for depth estimation using a single camera.
Funder
Institute of Information & Communications Technology Planning & Evaluation
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference25 articles.
1. Temperature monitoring techniques of power cable joints in underground utility tunnels using a fiber Bragg grating;Kim;ICT Express.,2022
2. Faugeras, O. (1993). Three-Dimensional Computer Vision: A Geometric Viewpoint, The MIT Press.
3. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms;Scharstein;Int. J. Comput. Vis.,2002
4. Kendall, A., Martirosyan, H., Dasgupta, S., and Henry, P. (2017, January 22–29). End-to-end learning of geometry and context for deep stereo regression. Proceedings of the IEEE International Conference on Computer Vision (ICCV) 2017, Venice, Italy.
5. Zhang, F., Prisacariu, V., Yang, R., and Torr, H.S.P. (2019, January 16–17). GA-Net: Guided Aggregation Net for End-To-end Stereo Matching. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.
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