Reducing False Alarms in Vision-Based Fire Detection

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.

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

2015 ◽  
Vol 24 (2) ◽  
pp. 261 ◽  
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.

scholarly journals Real Time Fire Detection System with Image Processing

2021 ◽  
Vol 3 ◽  
pp. 19-24
Author(s):  
Min Thu Soe ◽  
Thein Oak Kyaw Zaw ◽  
Wai Kit Wong
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Fire Detection ◽  
Accurate Method ◽  
False Alarms ◽  
Thermal Camera ◽  
Fire Fighters ◽  
The People ◽  
Fire Detection System ◽  
Thermal Video

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.

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

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.

scholarly journals Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2025 ◽  
Author(s):  
Jun Hong Park ◽  
Seunggi Lee ◽  
Seongjin Yun ◽  
Hanjin Kim ◽  
Won-Tae Kim
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Transfer ◽  
Detection System ◽  
Smart Cities ◽  
Fire Detection ◽  
Machine Learning Algorithms ◽  
Detection Accuracy ◽  
Detection Systems ◽  
Fire Detection System

A fire detection system requires accurate and fast mechanisms to make the right decision in a fire situation. Since most commercial fire detection systems use a simple sensor, their fire recognition accuracy is deficient because of the limitations of the detection capability of the sensor. Existing proposals, which use rule-based algorithms or image-based machine learning can hardly adapt to the changes in the environment because of their static features. Since the legacy fire detection systems and network services do not guarantee data transfer latency, the required need for promptness is unmet. In this paper, we propose a new fire detection system with a multifunctional artificial intelligence framework and a data transfer delay minimization mechanism for the safety of smart cities. The framework includes a set of multiple machine learning algorithms and an adaptive fuzzy algorithm. In addition, Direct-MQTT based on SDN is introduced to solve the traffic concentration problems of the traditional MQTT. We verify the performance of the proposed system in terms of accuracy and delay time and found a fire detection accuracy of over 95%. The end-to-end delay, which comprises the transfer and decision delays, is reduced by an average of 72%.

Smart Fire Detection System with Early Notifications Using Machine Learning

2017 ◽  
Vol 16 (02) ◽  
pp. 1750009 ◽  
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.

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

2012 ◽  
Vol 16 (6) ◽  
pp. 107-114 ◽  
Author(s):  
Kijun Lee ◽  
Hyeong Gweon Kim ◽  
Bong Woo Lee ◽  
Tae-Ok Kim ◽  
Dongil Shin
Keyword(s):  
Detection System ◽  
Fire Detection ◽  
False Alarms ◽  
Test Bed ◽  
Fire Detection System ◽  
Sensor Information

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

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.

scholarly journals IOT Based Fire Detection System

2021 ◽  
pp. 562-570
Author(s):  
Rashmi Vinod Patil ◽  
Sayali Fakira Jadhav ◽  
Kaveri Sitaram Kapse ◽  
Prof. M. B. Thombare ◽  
Prof. S. A. Talekar
Keyword(s):  
Detection System ◽  
Short Circuit ◽  
Text Message ◽  
Fire Detection ◽  
Android Application ◽  
Multiple Sensors ◽  
Detection Systems ◽  
Security Applications ◽  
System A ◽  
Fire Detection System

Fire Detection Systems are now widely used in various safety and security applications. The major amount of fire starts due to the electric short circuit. It leads to damage to property and also loss of life. To avoid that or to minimize the damage caused by fire outbreaks due to electric short circuits an IoT technology is used to control such a kind of risk. Traditional fire detection systems are not that effective and quick to alert the owner about fire, in case no one is present on the location. To overcome this problem in this paper we present the design and development of IoT based Fire Detection System. A system that combines qualities for fire, temperature and smoke detection, sending alert Text Message about the fire to the user along with onsite alarm(buzzer), updating temperature, humidity and smoke on ThingSpeak cloud every 15 seconds, and it also moves manually with the help of Android Application. The Fire Detection System consists of four main parts: Multiple sensors, communication system (Bluetooth, GSM, NodeMCU), motion planning (Manual patrolling), and Android application for manual patrolling of the system. This Fire Detection system can be used in college, school, office, and industry for safety purposes.

scholarly journals Fire Detection System Using Machine Learning

2021 ◽  
Vol 1916 (1) ◽  
pp. 012209
Author(s):  
A Arul ◽  
R S Hari Prakaash ◽  
R Gokul Raja ◽  
V Nandhalal ◽  
N Sathish Kumar
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Fire Detection ◽  
Fire Detection System
Sign in / Sign up

Export Citation Format

Share Document