A risk-informed and lightweight system for multi-modal fire detection
department or school: Applied Science, Faculty of
department or school: Engineering, School of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Yu, Jingshuo
issued: 2025-10-20T18:50:49Z
issued: 2025
related degree: Master of Applied Science - MASc
degree grantor: University of British Columbia
description: The urgent need for advanced fire detection systems stems from the increased intensity in fire events that cause massive property losses and irreversible damage. To overcome the limitations of traditional fire detection systems such as smoke detectors, computer vision (CV) algorithms have been adopted to improve fire detection accuracy. A CV-based fire detection system takes images from fire monitoring camera as input and output fire-related information. To identify the installation location of fire monitoring cameras, this research performed compartment-level fire risk analysis based on fire risk analysis method for engineering (FRAME). Relevant fire risk factors were quantified using a building information modeling (BIM) model, and their relative importance was evaluated through the analytic hierarchy process (AHP). The resulting compartment-specific fire risk values provide data-driven guidance for deploying cameras in high-risk areas of a building. The case study result of a residential building aligns with historical fire incident statistics, which validates the proposed framework. In terms of fire detection model, when compared to single-modal model, multi-modal model has gained attention because it leverages the richer information presented in both RGB and thermal images. However, prevalent multi-modal fire detection methods significantly increase model complexity by requiring two separate streams in the backbone to process RGB and thermal images independently. To address this issue, this thesis proposes a 4-channel single-stream fire detection method based on YOLOv5, which concatenates RGB and thermal images to form the required 4-channel input. Comparison experiments with dual-stream YOLOv5 models using add fusion and transformer fusion demonstrate that the four-channel single-stream model reduces the model complexity while improving detection accuracy. To further enhance the accuracy of multi-modal fire detection and reduce model complexity, this study red…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/92669
note: A RISK-INFORMED AND LIGHTWEIGHT SYSTEM FOR MULTI-MODAL FIRE DETECTION by Jingshuo Yu B.Eng., Wuhan University of Science and Technology, 2022 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Civil Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) October 2025 © Jingshuo Yu, 2025 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis entitled: A risk-informed and lightweight system for multi-modal fire detection submitted by Jingshuo Yu in partial fulfilment of the requirements for the degree of Master of Applied Science Examining Committee: Dr. Qian Chen, School of Engineering Supervisor Dr. Zheng Liu, School of Engineering Supervisory Committee Member Dr. Suliman Gargoum, School of Engineering Supervisory Committee Member Dr. Xiaoping Shi, Faculty of Computer Science, Mathematics, Physics and Statistics University Examiner iii Abstract The urgent need for advanced fire detection systems stems from the increased intensity in fire events that cause massive property losses and irreversible damage. To overcome the limitations of traditional fire detection systems such as smoke detectors, computer vision (CV) algorithms have been adopted to improve fire detection accuracy. A CV-based fire detection system takes images from fire monitoring camera as input and output fire-related information. To identify the installation location of fire monitoring cameras, this research performed compartment-level fire risk analysis based on fire risk analysis method for engineering (FRAME). Relevant fire risk factors were quantified using a building information modeling (BIM) model, and their relative importance was evaluated through the analytic hierarchy process (AHP). The resulting compartment-specific fire risk values provide data-driven guidance for deploying cameras in high-risk areas of a building. The c…
has type: Thesis/Dissertation
date issued: 2025-11
is shown at: 10.14288/1.0450490
language: eng
degree discipline: Civil Engineering
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
contributor: Chen, Qian
type: Text
identifier URI: http://hdl.handle.net/2429/92669