Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)                   IJEEE 2025, 21(2): 3663-3663 | Back to browse issues page

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Mansor H, Abdul Shukor S A, Chen Keng R W, Khalid N S. Detection of Indoor Building Lighting Fixtures in Point Cloud Data using SDBSCAN. IJEEE 2025; 21 (2) :3663-3663
URL: http://ijeee.iust.ac.ir/article-1-3663-en.html
Detection of Indoor Building Lighting Fixtures in Point Cloud Data using SDBSCAN
Humairah Mansor , Shazmin Aniza Abdul Shukor , Razak Wong Chen Keng , Nurul Syahirah Khalid
Abstract:   (133 Views)
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.
Keywords: Clustering, Fixtures, Heuristic, Point Cloud Data, Segmentation.
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Type of Study: Only For Articles of ELECRiS 2024 | Subject: Machine Learning
Received: 2024/12/31 | Revised: 2025/03/14 | Accepted: 2025/02/20
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.