NEEDLE IN A HAYSTACK: FEASIBILITY OF IDENTIFYING SMALL SAFETY ASSETS FROM POINT CLOUDS USING DEEP LEARNING
Asset management systems are beneficial for maintaining building infrastructure and can be used to keep up-to-date records of relevant safety assets, such as smoke detectors, exit signs, and fire extinguishers. Existing methods for locating and identifying these assets in buildings primarily rely on...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2023-12-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-annals.copernicus.org/articles/X-1-W1-2023/461/2023/isprs-annals-X-1-W1-2023-461-2023.pdf |
| _version_ | 1797403688026767360 |
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| author | G. Anjanappa S. Nikoohemat S. Oude Elberink R. L. Vôute V. V. Lehtola |
| author_facet | G. Anjanappa S. Nikoohemat S. Oude Elberink R. L. Vôute V. V. Lehtola |
| author_sort | G. Anjanappa |
| collection | DOAJ |
| description | Asset management systems are beneficial for maintaining building infrastructure and can be used to keep up-to-date records of relevant safety assets, such as smoke detectors, exit signs, and fire extinguishers. Existing methods for locating and identifying these assets in buildings primarily rely on surveys and images, which only provide 2D locations and can be tedious for large-scale structures. Indoor point clouds, which can be captured quickly for buildings using mobile scanning techniques, can provide us with 3D asset locations. In this paper, we study the feasibility of using 3D point clouds of buildings combined with deep learning techniques to identify safety-related assets, particularly small-sized assets like fire switches and exit signs. We adopt the state-of-the-art Deep Learning network, Kernel Point-Fully Convolutional Network (KP-FCNN), to identify these assets through semantic segmentation. Using the obtained results, we create a 3D-geometry model of the building with assets pinpointed, providing scene semantics and delivering more value. Our method is tested using three different point cloud datasets obtained from a depth camera, a mobile laser scanner, and an iPhone lidar sensor. |
| first_indexed | 2024-03-09T02:42:01Z |
| format | Article |
| id | doaj.art-1d6d821c030049d290f6ede475784c51 |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-03-09T02:42:01Z |
| publishDate | 2023-12-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-1d6d821c030049d290f6ede475784c512023-12-06T02:27:12ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502023-12-01X-1-W1-202346146810.5194/isprs-annals-X-1-W1-2023-461-2023NEEDLE IN A HAYSTACK: FEASIBILITY OF IDENTIFYING SMALL SAFETY ASSETS FROM POINT CLOUDS USING DEEP LEARNINGG. Anjanappa0S. Nikoohemat1S. Oude Elberink2R. L. Vôute3V. V. Lehtola4Dept. of Earth Observation Science, Faculty ITC, University of Twente, Enschede, The NetherlandsDept. of Earth Observation Science, Faculty ITC, University of Twente, Enschede, The NetherlandsDept. of Earth Observation Science, Faculty ITC, University of Twente, Enschede, The NetherlandsCGI Inc, The NetherlandsDept. of Earth Observation Science, Faculty ITC, University of Twente, Enschede, The NetherlandsAsset management systems are beneficial for maintaining building infrastructure and can be used to keep up-to-date records of relevant safety assets, such as smoke detectors, exit signs, and fire extinguishers. Existing methods for locating and identifying these assets in buildings primarily rely on surveys and images, which only provide 2D locations and can be tedious for large-scale structures. Indoor point clouds, which can be captured quickly for buildings using mobile scanning techniques, can provide us with 3D asset locations. In this paper, we study the feasibility of using 3D point clouds of buildings combined with deep learning techniques to identify safety-related assets, particularly small-sized assets like fire switches and exit signs. We adopt the state-of-the-art Deep Learning network, Kernel Point-Fully Convolutional Network (KP-FCNN), to identify these assets through semantic segmentation. Using the obtained results, we create a 3D-geometry model of the building with assets pinpointed, providing scene semantics and delivering more value. Our method is tested using three different point cloud datasets obtained from a depth camera, a mobile laser scanner, and an iPhone lidar sensor.https://isprs-annals.copernicus.org/articles/X-1-W1-2023/461/2023/isprs-annals-X-1-W1-2023-461-2023.pdf |
| spellingShingle | G. Anjanappa S. Nikoohemat S. Oude Elberink R. L. Vôute V. V. Lehtola NEEDLE IN A HAYSTACK: FEASIBILITY OF IDENTIFYING SMALL SAFETY ASSETS FROM POINT CLOUDS USING DEEP LEARNING ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | NEEDLE IN A HAYSTACK: FEASIBILITY OF IDENTIFYING SMALL SAFETY ASSETS FROM POINT CLOUDS USING DEEP LEARNING |
| title_full | NEEDLE IN A HAYSTACK: FEASIBILITY OF IDENTIFYING SMALL SAFETY ASSETS FROM POINT CLOUDS USING DEEP LEARNING |
| title_fullStr | NEEDLE IN A HAYSTACK: FEASIBILITY OF IDENTIFYING SMALL SAFETY ASSETS FROM POINT CLOUDS USING DEEP LEARNING |
| title_full_unstemmed | NEEDLE IN A HAYSTACK: FEASIBILITY OF IDENTIFYING SMALL SAFETY ASSETS FROM POINT CLOUDS USING DEEP LEARNING |
| title_short | NEEDLE IN A HAYSTACK: FEASIBILITY OF IDENTIFYING SMALL SAFETY ASSETS FROM POINT CLOUDS USING DEEP LEARNING |
| title_sort | needle in a haystack feasibility of identifying small safety assets from point clouds using deep learning |
| url | https://isprs-annals.copernicus.org/articles/X-1-W1-2023/461/2023/isprs-annals-X-1-W1-2023-461-2023.pdf |
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