Detection of Indoor Building Lighting Fixtures in Point Cloud Data using SDBSCAN
Building fixtures like lighting are very important to be modelled, especially when a higher level of modelling details is required for planning indoor renovation. LIDAR is often used to capture these details due to its capability to produce dense information. However, this led to the high amount of...
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| Format: | Article |
| Language: | English |
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Iran University of Science and Technology
2025-06-01
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| Series: | Iranian Journal of Electrical and Electronic Engineering |
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| Online Access: | http://ijeee.iust.ac.ir/article-1-3663-en.pdf |
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| author | Humairah Mansor Shazmin Aniza Abdul Shukor Razak Wong Chen Keng Nurul Syahirah Khalid |
| author_facet | Humairah Mansor Shazmin Aniza Abdul Shukor Razak Wong Chen Keng Nurul Syahirah Khalid |
| author_sort | Humairah Mansor |
| collection | DOAJ |
| description | Building fixtures like lighting are very important to be modelled, especially when a higher level of modelling details is required for planning indoor renovation. LIDAR is often used to capture these details due to its capability to produce dense information. However, this led to the high amount of data that needs to be processed and requires a specific method, especially to detect lighting fixtures. This work proposed a method named Size Density-Based Spatial Clustering of Applications with Noise (SDBSCAN) to detect the lighting fixtures by calculating the size of the clusters and classifying them by extracting the clusters that belong to lighting fixtures. It works based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN), where geometrical features like size are incorporated to detect and classify these lighting fixtures. The final results of the detected lighting fixtures to the raw point cloud data are validated by using F1-score and IoU to determine the accuracy of the predicted object classification and the positions of the detected fixtures. The results show that the proposed method has successfully detected the lighting fixtures with scores of over 0.9. It is expected that the developed algorithm can be used to detect and classify fixtures from any 3D point cloud data representing buildings. |
| format | Article |
| id | doaj-art-17c40e9eea754f3bb9f3da51bcbf9899 |
| institution | DOAJ |
| issn | 1735-2827 2383-3890 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Iran University of Science and Technology |
| record_format | Article |
| series | Iranian Journal of Electrical and Electronic Engineering |
| spelling | doaj-art-17c40e9eea754f3bb9f3da51bcbf98992025-08-20T02:40:22ZengIran University of Science and TechnologyIranian Journal of Electrical and Electronic Engineering1735-28272383-38902025-06-0121236633663Detection of Indoor Building Lighting Fixtures in Point Cloud Data using SDBSCANHumairah Mansor0Shazmin Aniza Abdul Shukor1Razak Wong Chen Keng2Nurul Syahirah Khalid3 Faculty of Electrical Engineering & Technology and Centre of Excellence for Intelligent Robotics & Autonomous Systems, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia. Faculty of Electrical Engineering & Technology and Centre of Excellence for Intelligent Robotics & Autonomous Systems, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia. Geodelta Systems Sdn. Bhd., 22, Jalan SS 20/11, Damansara Utama, Petaling Jaya 47400, Malaysia. Faculty of Electrical Engineering & Technology and Centre of Excellence for Intelligent Robotics & Autonomous Systems, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia. Building fixtures like lighting are very important to be modelled, especially when a higher level of modelling details is required for planning indoor renovation. LIDAR is often used to capture these details due to its capability to produce dense information. However, this led to the high amount of data that needs to be processed and requires a specific method, especially to detect lighting fixtures. This work proposed a method named Size Density-Based Spatial Clustering of Applications with Noise (SDBSCAN) to detect the lighting fixtures by calculating the size of the clusters and classifying them by extracting the clusters that belong to lighting fixtures. It works based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN), where geometrical features like size are incorporated to detect and classify these lighting fixtures. The final results of the detected lighting fixtures to the raw point cloud data are validated by using F1-score and IoU to determine the accuracy of the predicted object classification and the positions of the detected fixtures. The results show that the proposed method has successfully detected the lighting fixtures with scores of over 0.9. It is expected that the developed algorithm can be used to detect and classify fixtures from any 3D point cloud data representing buildings.http://ijeee.iust.ac.ir/article-1-3663-en.pdfclusteringfixturesheuristicpoint cloud datasegmentation. |
| spellingShingle | Humairah Mansor Shazmin Aniza Abdul Shukor Razak Wong Chen Keng Nurul Syahirah Khalid Detection of Indoor Building Lighting Fixtures in Point Cloud Data using SDBSCAN Iranian Journal of Electrical and Electronic Engineering clustering fixtures heuristic point cloud data segmentation. |
| title | Detection of Indoor Building Lighting Fixtures in Point Cloud Data using SDBSCAN |
| title_full | Detection of Indoor Building Lighting Fixtures in Point Cloud Data using SDBSCAN |
| title_fullStr | Detection of Indoor Building Lighting Fixtures in Point Cloud Data using SDBSCAN |
| title_full_unstemmed | Detection of Indoor Building Lighting Fixtures in Point Cloud Data using SDBSCAN |
| title_short | Detection of Indoor Building Lighting Fixtures in Point Cloud Data using SDBSCAN |
| title_sort | detection of indoor building lighting fixtures in point cloud data using sdbscan |
| topic | clustering fixtures heuristic point cloud data segmentation. |
| url | http://ijeee.iust.ac.ir/article-1-3663-en.pdf |
| work_keys_str_mv | AT humairahmansor detectionofindoorbuildinglightingfixturesinpointclouddatausingsdbscan AT shazminanizaabdulshukor detectionofindoorbuildinglightingfixturesinpointclouddatausingsdbscan AT razakwongchenkeng detectionofindoorbuildinglightingfixturesinpointclouddatausingsdbscan AT nurulsyahirahkhalid detectionofindoorbuildinglightingfixturesinpointclouddatausingsdbscan |