Test Bed Design of Fire Detection System Based on Multi-Sensor Information for Reduction of False Alarms

Fire detectionsystemby image processing is a growing research in this era. There are many methods used to detect fire out, butstill need to develop an accurate method to detect fire without false alarms. This is due to the fact that many methods used RGB colour mode for detection. In this paper, mainly focuson detecting the fire effectively using thermal video from a thermal camera while in the same time the system will alert the people if fire was detected,and also observed the speed of the fire.This will enormouslybenefitto the fire fighters.With thissystem, thefire can be detected effectively while alerting the people and giving valuable information to the fire fighters fortheir job more effectively.

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A smart fire detection system using iot technology with automatic water sprinkler

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v11i4.pp2994-3002 ◽

2021 ◽

Vol 11 (4) ◽

pp. 2994

Author(s):

Hamood Alqourabah ◽

Amgad Muneer ◽

Suliman Mohamed Fati

Keyword(s):

Data Exchange ◽

Emergency Services ◽

Detection System ◽

Real Life ◽

Fire Department ◽

Fire Detection ◽

False Alarms ◽

Real Life Data ◽

Long Time ◽

Fire Detection System

House combustion is one of the main concerns for builders, designers, and property residents. Singular sensors were used for a long time in the event of detection of a fire, but these sensors can not measure the amount of fire to alert the emergency response units. To address this problem, this study aims to implement a smart fire detection system that would not only detect the fire using integrated sensors but also alert property owners, emergency services, and local police stations to protect lives and valuable assets simultaneously. The proposed model in this paper employs different integrated detectors, such as heat, smoke, and flame. The signals from those detectors go through the system algorithm to check the fire's potentiality and then broadcast the predicted result to various parties using GSM modem associated with the system. To get real-life data without putting human lives in danger, an IoT technology has been implemented to provide the fire department with the necessary data. Finally, the main feature of the proposed system is to minimize false alarms, which, in turn, makes this system more reliable. The experimental results showed the superiority of our model in terms of affordability, effectiveness, and responsiveness as the system uses the Ubidots platform, which makes the data exchange faster and reliable.

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Reducing False Alarms in Vision-Based Fire Detection

Applied Video Processing in Surveillance and Monitoring Systems - Advances in Multimedia and Interactive Technologies ◽

10.4018/978-1-5225-1022-2.ch012 ◽

2017 ◽

pp. 263-290

Author(s):

Neethidevan Veerapathiran ◽

Anand S.

Keyword(s):

Video Processing ◽

Detection System ◽

Chemical Properties ◽

Fire Detection ◽

False Alarms ◽

Video Signals ◽

Detection Systems ◽

Physical Sensors ◽

Single Feature ◽

Fire Detection System

Computer vision techniques are mainly used now a days to detect the fire. There are also many challenges in trying whether the region detected as fire is actually a fire this is perhaps mainly because the color of fire can range from red yellow to almost white. So fire region cannot be detected only by a single feature and many other features (i.e.) color have to be taken into consideration. Early warning and instantaneous responses are the preventing ideas to avoid losses affecting environment as well as human causalities. Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms. In order to reduce false alarms of conventional fire detection systems, system make use of vision based fire detection system. This chapter discuss about the fundamentals of videos, various issues in processing video signals, various algorithms for video processing using vision techniques.

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Smart Fire Detection System with Early Notifications Using Machine Learning

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026817500092 ◽

2017 ◽

Vol 16 (02) ◽

pp. 1750009 ◽

Cited By ~ 1

Author(s):

Mohammad Sultan Mahmud ◽

Md. Shohidul Islam ◽

Md. Ashiqur Rahman

Keyword(s):

Machine Learning ◽

Detection System ◽

Fire Department ◽

Fire Detection ◽

Machine Learning Algorithms ◽

False Alarms ◽

Police Station ◽

Local Police ◽

Long Time ◽

Fire Detection System

House fire is one of the major concerns for designers, builders, and residents of property. In the case of detecting fire, individual sensors have been used for a long time, but they cannot detect the level of fire and notify the emergency response units. To solve this problem, this study attempts to propose an intelligent early fire detection system that would not only detect the fire by using integrated sensors but also notify the appropriate authorities including fire department, ambulance services, and local police station simultaneously to protect valuable lives and properties. Signals from the integrated detectors e.g., heat, smoke, and flame go through the machine learning algorithms to check the potentiality of the fire as well as broadcast the predicted result to various parties using a GSM modem. To consolidate the predicted output, structured forest for fast edge detection has also been applied. The final outcome of this development also minimized false alarms, thus making this system more reliable.

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Development of System for Responding to Fire in Space Outside Traditional Market

Korean Society of Hazard Mitigation ◽

10.9798/kosham.2020.20.5.105 ◽

2020 ◽

Vol 20 (5) ◽

pp. 105-112

Author(s):

Yoonbeom So ◽

Yonghan Kim ◽

Sehong Min

Keyword(s):

Detection System ◽

Fire Detection ◽

False Alarms ◽

Independent State ◽

Fire Station ◽

Traditional Market ◽

Fire Detector ◽

Fire Detection System ◽

Fire Stations ◽

Command Center

As severe fire occurs and spreads successively in a traditional market, the need for the prompt response to the fire in the space outside the traditional market becomes more urgent. The fire-detection system used for the Daegu S-traditional market has the problems of having no adaptability to outside space, achieving fire detection reliability for the inside space owing to false alarms, and for operating the critical fire-detection signal delivered from the fire detector through the fire command center to the fire station in the independent state without interconnectedness. Hence, in this study, a reliable image-based fire-detection system prepared for reducing the fire risks of the traditional market, including the problems of the occurrence of unwanted alarms and adaptability to fire detection in the outside space, is developed. A demonstration-based real-time-situation notice system was connected from the fire detector to the fire command center, fire stations, merchants, and residents.

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The influence of external factors on false alarms in an infrared fire detection system

International Journal of Wildland Fire ◽

10.1071/wf13200 ◽

2015 ◽

Vol 24 (2) ◽

pp. 261 ◽

Cited By ~ 6

Author(s):

Pedro Canales Mengod ◽

José Andrés Torrent Bravo ◽

Leticia López Sardá

Keyword(s):

Detection System ◽

Fire Detection ◽

Time Of Day ◽

External Factors ◽

False Alarms ◽

Detection Algorithms ◽

Detection Systems ◽

Camera Orientation ◽

Fire Detection System

There have been many studies on the use of different automatic wildfire detection systems, yet few long-term analyses of any of these techniques have been reported. In this paper we present the results obtained from the study of an infrared fire detection system that has been working in the field for more than 10 years, over which period it produced 10 519 false alarms. This article gives a brief description of the system and discusses the false alarms, showing that factors that are often not taken into account in the development of fire detection algorithms, such as camera orientation, the type of surface being monitored, or the time of day, can lead to false alarms being triggered.

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