Volume 22, Issue 3 (September 2026)                   IJEEE 2026, 22(3): 3733-3733 | Back to browse issues page

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Ahmed K, Gomes B R, Hossain S J, Musa E K, Bhoyan F H, Mehedi M H K et al . Reducing False Alarms in Fire Detection Systems with YOLOv11 and Multi-Sensor Validation. IJEEE 2026; 22 (3) :3733-3733
URL: http://ijeee.iust.ac.ir/article-1-3733-en.html
Reducing False Alarms in Fire Detection Systems with YOLOv11 and Multi-Sensor Validation
Kausar Ahmed , Bibhor Regan Gomes , S.M. Jobair Hossain , Emran Khan Musa , Fuyad Hasan Bhoyan , Md Humaion Kabir Mehedi , Jia Uddin
Abstract:   (124 Views)
Fires in indoor spaces such as residential and office buildings pose significant threats to human lives and property, causing substantial damage each year. Early and accurate fire detection plays a critical role in mitigating these risks and ensuring timely responses. However, conventional methods such as smoke sensors, temperature indicators, and standalone computer vision models suffer from limitations like false alarms, delayed detection, and high hardware demands. To address these challenges, we propose a novel three-layer verification framework for indoor fire detection to reduce false alarms, integrating smoke sensors, computer vision, and temperature monitoring into a multi-modal validation framework. The process begins with smoke sensors detecting potential fire incidents. The custom-trained YOLOv11n computer vision model verifies the detection using predefined thresholds, allowing immediate response without waiting for temperature escalation. If the computer vision model does not confirm the fire, the system initiates a temperature check as a final validation layer. Experimental evaluation of our model demonstrates a significantly high precision of 0.979 and a recall of 0.971. This layered approach ensures comprehensive detection, balancing reliability and resource efficiency. Our proposed hybrid AI-physical systematic framework demonstrates significant potential in reducing false alarms, improving detection accuracy, and prioritizing methodological scalability over industrial hardware. It lays the foundation for more reliable and energy-efficient fire safety solutions in smart buildings and industrial safety applications.
Keywords: False Alarm, Internet of Things (IoT), Computer Vision, Indoor Fire.
Full-Text [PDF 974 kb]   (54 Downloads)    
Type of Study: Research Paper | Subject: Electronic Components Technology
Received: 2025/02/08 | Revised: 2026/05/18 | Accepted: 2025/12/27
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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.