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Iranian Journal of Electrical and Electronic Engineering، جلد ۲۱، شماره ۲، صفحات ۳۶۶۳-۳۶۶۳
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عنوان انگلیسی Detection of Indoor Building Lighting Fixtures in Point Cloud Data using SDBSCAN
چکیده انگلیسی مقاله 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.
کلیدواژه‌های انگلیسی مقاله Clustering, Fixtures, Heuristic, Point Cloud Data, Segmentation.
نویسندگان مقاله | Humairah Mansor
Faculty of Electrical Engineering & Technology and Centre of Excellence for Intelligent Robotics & Autonomous Systems, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia.


| Shazmin Aniza Abdul Shukor
Faculty of Electrical Engineering & Technology and Centre of Excellence for Intelligent Robotics & Autonomous Systems, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia.


| Razak Wong Chen Keng
Geodelta Systems Sdn. Bhd., 22, Jalan SS 20/11, Damansara Utama, Petaling Jaya 47400, Malaysia.


| Nurul Syahirah Khalid
Faculty of Electrical Engineering & Technology and Centre of Excellence for Intelligent Robotics & Autonomous Systems, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia.
نشانی اینترنتی http://ijeee.iust.ac.ir/browse.php?a_code=A-10-5548-1&slc_lang=en&sid=1
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کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده Machine Learning
نوع مقاله منتشر شده Only For Articles of ELECRiS 2024
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