Automatic Fire Detection Research Articles

Automatic Fire Detection and Extinction with Infrared Multispectrum Electro-Optical Technology with Watch-Dog Timer Control, for a 220KV to 33KV Transformer

The present research aims to design an automatic fire detection and extinction system, developed with infrared multi-spectrum electro-optical technology with watch-dog timer control, for an electrical transformer from 220KV to 33KV. Upon its development, it is concluded that the automatic detection and extinction system has a deluge system with sprayed water, which will be activated by a detection system with flame sensors, this system has infrared multispectrum Electro-Optical Technology and will be controlled by through the Timer Watch-Dog, which will automatically detect and report any failure in the state-of-theart microprocessor. By subjecting the detection and extinguishing system to operational and functional tests, an optimal response of the deluge sprinklers was obtained, through the pressure and flow parameters, also a coefficient of determination R2 equal to 0.991 is obtained, which represents that the design is optimal, evidencing feasibility from the operational and functional point of view. Keywords— Detection, Extinction, Automatic, ElectroOptical, Multispectral, Infrared, Timer Watch-Dog, Transformer

Design Analysis and Implementation of Automatic Fire Extinguishing System Using ATmega16 Microcontroller as Control

Aim: The aim of this research is to design an automatic fire detection and control.
 Methodology: The system consists of two sensors, microcontroller, buzzer and a pump/sprinkler. An LM35 integrated chip has been used as temperature sensor while MQ-2 gas sensor has been used as smoke sensor. All sensors are connected to the microcontroller through an input/output port. The controlling software for the whole system was designed in C programming language. The popular high performance, low power 8-bit microcontroller from the AVR family microcontrollers has been used. The system was finally tested by introducing fire parameters (smoke and temperature) close to the smoke and temperature detectors respectively. When the parameters go above the set level in the detectors, audio alarm, light indicator and pump/sprinkler were activated. The measured temperature of the system was compared with the reference temperature. 
 Results: The result showed that there was a mean deviation of 1.550C between the measured values and reference values which served as the control. 
 Conclusion: The constructed system is compact and easy to install. The constructed fire control device is quite cheap and affordable to every category of person.

Deep Learning-Based Instance Segmentation for Indoor Fire Load Recognition

Accurate fire load (combustible objects) information is crucial for safety design and resilience assessment of buildings. Traditional fire load acquisition methods, such as fire load survey, which are time-consuming, tedious, and error-prone, failed to adapt to dynamic changed indoor scenes. As a starting point of automatic fire load estimation, fast recognition and detection of indoor fire load are important. Thus, this research proposes a computer vision-based method to automatically detect indoor fire loads using deep learning-based instance segmentation. First, indoor elements are classified into different categories according to their material composition. Next, an image dataset of indoor scenes with instance annotations is developed. Finally, a deep learning model, based on Mask R-CNN, is developed and trained using transfer learning to detect fire loads in images. Experimental results show that our model achieves promising accuracy, as measured by an average precision (AP) of 40.5% and AP50 of 59.2%, for instance segmentation on the dataset. A comparison with manual detection demonstrates the method’s high efficiency as it can detect fire load 1200 times faster than humans. This research contributes to the body of knowledge 1) a novel method of high accuracy and efficiency for automated fire load recognition in indoor environments based on instance segmentation; 2) training techniques for a deep learning model in a relatively small dataset of indoor images which includes complex scenes and a variety of instances; and 3) an image dataset with annotations of indoor fire loads. Although instance segmentation has been applied for several years, this is a pioneering research on using it for automated indoor fire load recognition, which paves the foundation for automatic fire load estimation and resilience assessment for the built environment.

Fire Safety Management in High Rise Buildings of Lahore, Pakistan

Basic objective of this study was to assess the degree to which the high rise buildings situated in Lahore are in compliance with the fire safety standards and SOPs regarding evacuation plans. Quantitative approach was adopted to meet the objective of the study. Ten high rise buildings situated in Lahore city were taken as sample. The buildings were selected on the basis of their size, height, and area. The selected buildings included new and old buildings, commercial and residential buildings so that the variations regarding fire safety standards could be measured. A comprehensive survey questionnaire was developed. Results of the study show that besides various critical incidents, no proper measures are taken on scientific grounds to ensure prevention or at least taking rapid response to minimize losses. There exist no fire safety risk assessments, zero ongoing monitoring, no ongoing fire safety training or awareness program, inadequate or dysfunctional fire extinguishing instruments, no building fire safety control, no automatic fire detection and alarm system, no ongoing fire evacuation drills, and the access of fire brigade to the fire sight is inadequate. Recommendations of the study may prove highly productive in constructing safe buildings from safety point of view.

Prediction and Evaluation of Emergency Broadcasting Sound and Speech Intelligibility for Safety Zones in High-rise Buildings

As construction of high-rise buildings is increasing, the installation of a fire safety zone for the safe evacuation of residents has become mandatory. Even for building complexes with an underground connection between 30 and 49 stories, a fire safety zone must be installed when the residential density exceeds 1.5 people per square meter. In the fire safety zone, an automatic fire detection system and a system to assist radio communication must be installed. In addition, an emergency broadcasting system must be installed to announce the fire situation and to guide the safe evacuation of the residents. The fire safety zone was constructed with non-combustible material to minimize flames or smoke and was furnished to maintain a very low fire load. However, non-combustible materials have very low sound absorption characteristics; therefore, they usually reflect the sound energy into the spaces. In spaces consisting of sound reflective materials, the emergency broadcasting sounds reverberated, making it difficult to understand the meaning of the broadcasted sound. In this study, the room acoustics characteristics for fire safety zones in a high-rise building, represented as reverberation time and speech transmission index, were predicted using a certified room acoustics simulation method and evaluation of speech intelligibility. As a result of predicting the acoustic characteristics of the safety zone, it was found that the space had quite long reverberation and speech intelligibility was low, making it difficult to clearly transmit the emergency broadcast sound. When a safety zone was set up in a part of the machinery room, it was confirmed that it was difficult to clearly transmit the emergency broadcast sound, even when the background noise level was louder than 70 dB(A). To deliver a clear emergency broadcast sound in the safety zone, it is necessary to use inorganic sound-absorbing materials and to properly block noise in the machine room. In addition, it is necessary to establish guidelines for regulation of the speech transmission index for emergency broadcasting systems.

A Comparison and Analysis of Domestic and Foreign Standards Related to Unwanted Fire Alarms

As unwanted fire alarms within the automatic fire detection system increase, fire-fighting power gets wasted. This is recognized as important because it causes a decrease in the evacuation reliability of the occupancy. Therefore, in order to develop measures to reduce unwanted fire alarms, foreign (United States and, United Kingdom) and domestic standards related to unwanted fire alarms were compared and analyzed in the present study. Through the analysis the problems with the standards were identified as uncertainty regarding the detecting space and radius of the smoke detector, the absence of a statistical database (DB) for unwanted fire alarms, the absence of a protocol for managing unwanted fire alarms, and the absence of testing standards for unwanted fire alarms based on scenarios. Consequently, it is proposed that to deal with the problems, it is necessary to ascertain the detecting space and radius of the smoke detector, to propose unwanted fire alarm protection codes for database (DB) system construction, to supplement the fire safety management log by developing related manuals, and to develop unwanted fire alarm testing standards of Korean type based on scenarios.

Automatic Fire and Smoke Detection Method for Surveillance Systems Based on Dilated CNNs

The technologies underlying fire and smoke detection systems play a crucial role in ensuring and delivering optimal performance in modern surveillance environments. In fact, fire can cause significant damage to lives and properties. Considering that the majority of cities have already installed camera-monitoring systems, this encouraged us to take advantage of the availability of these systems to develop cost-effective vision detection methods. However, this is a complex vision detection task from the perspective of deformations, unusual camera angles and viewpoints, and seasonal changes. To overcome these limitations, we propose a new method based on a deep learning approach, which uses a convolutional neural network that employs dilated convolutions. We evaluated our method by training and testing it on our custom-built dataset, which consists of images of fire and smoke that we collected from the internet and labeled manually. The performance of our method was compared with that of methods based on well-known state-of-the-art architectures. Our experimental results indicate that the classification performance and complexity of our method are superior. In addition, our method is designed to be well generalized for unseen data, which offers effective generalization and reduces the number of false alarms.

Problems and Improvement Measures of ｢Special Act on the Safety Control of Publicly Used Establishments｣

The examination of the transition process of the「Special Act on the Safety Control of Publicly Used Establishments」(hereinafter referred to as the "Publicly Used Establishments Act") shows that the related laws are being revised when major accidents result in human or property damage. People who intend to conduct a multi-use business must install safety facilities, etc., prescribed by Presidential Decree, in accordance with the standards prescribed by Ordinance of the Ministry of Public Administration and Security. In this study, the legal standards, construction and safety accident cases that could raise issues in the current Publicly Used Establishment-related laws were investigated. As a result, we identified issues such as emergency exit opening and closing problems, legal blind spot issues in food courts, fire alarm system limitations, and the absence of a ventilation system. To tackle these issues, improvement measures such as adding restriction regulations for automatic opening and closing devices, an automatic fire detection system, food courts in emergency exits, as well as visual alarm device regulations, and the establishment of ventilation system regulations were suggested.

DEVELOPMENT OF COMPUTER VISION-BASED AUTOMATIC FLAME DETECTION ALGORITHM USING MATLAB SOFTWARE ENVIRONMENT

DEVELOPMENT OF COMPUTER VISION-BASED AUTOMATIC FLAME DETECTION ALGORITHM USING MATLAB SOFTWARE ENVIRONMENT

A Fire Danger Index for the early detection of areas vulnerable to wildfires in the Eastern Mediterranean region

Wildfires continue to be a major factor of disturbance to Mediterranean ecosystems, and are often associated with significant losses of properties and human lives. Fast fire detection and suppression within the first few minutes after ignition are crucial to successfully managing wildfires and preventing their potentially catastrophic consequences. In this study, remote-sensing methods and data were integrated wih fire behavior simulation and field data to develop a Fire Danger Index (FDI) that can be used to detect the areas most vulnerable to wildfires. This FDI will be integrated into an automatic fire detection system that utilizes optical and thermal land cameras and an unmanned aerial vehicle. The FDI was calculated for a nature reserve in Southern Greece based on fire behavior, pyric history, and anthropogenic influence. Fire behavior was estimated using the FlamMap fire simulation model, while the fuel types to include in the model were determined using state-of-the-art remote-sensing methods and field data. The pyric history was represented by point data on fire occurrences over a period of 40 years. The anthropogenic influence was estimated based on an inverse relationship of this influence with the Euclidean distance from roads and settlements. The calculated FDI demonstrated that a large part of the reserve, including its most ecologically important ecosystems, is highly vulnerable to wildfires. Integrating the FDI into the automatic fire detection system is expected to significantly improve its detection accuracy.

Case study on Designing a Comprehensive Fire Protection System for KY Power Station

The KY Power Station relies on two gas turbines to generate electrical energy. In addition, fuel storage is also required to ensure uninterrupted power supplies. As a power generation plant and the existence of fuel storage, an efficient fire protection system is essential. This paper presented a comprehensive review of the existing research on fire protection systems. Utilising the reviewed as basis, this paper also described the five types of KY Power Station fire protection systems as the case study. Specifically, the five fire protection systems are; Water Spray Fixed System, Foam Protection System, Automatic Carbon Dioxide Extinguishing System, Pressurized Fire Hydrant and lastly Automatic Fire Detection and Alarm System. Even though a station may require more than one fire protection systems as illustrated by the case study, this paper observed that the existing researches focused mainly on designing the individual system of different fire protection system according to standard. This paper found that there is lacked of research on optimising these individual fire protection systems. The optimisation usually requires development of mathematical model. Therefore, there is a need for development of mathematical model for the individual fire protection system. These different types of fire protection systems are independent but they are equally important. Thus, there is also a need to look into global optimisation in designing the fire protection system.

Research on Vehicle Exterior Fire Suppression Techniques

This paper studies automatic vehicle exterior fire detection and suppression techniques. The main difficulties include: 1) complex and changeable environment outside the vehicle; 2) low sensitivity of the fire detector; 3) quick dissipation of fire extinguishing agent in the open space outside the vehicle; 4) extinguishing agent nozzle blocking due to dust and sand. This paper selects a special ultra-fine dry powder fire extinguishing agent for automobiles to design the protective nozzle and to study the automatic detection controller with the functions of condition monitoring and fault diagnosis, with an aim to improve fire suppression reliability. Based on the virtual and real vehicle tests, it is verified that the fire extinguishing effect of the designed system meets the design requirements.

Conceptual Framework of an Intelligent Decision Support System for Smart City Disaster Management

In order to protect human lives and infrastructure, as well as to minimize the risk of damage, it is important to predict and respond to natural disasters in advance. However, currently, the standardized disaster response system in South Korea still needs further advancement, and the response phase systems need to be improved to ensure that they are properly equipped to cope with natural disasters. Existing studies on intelligent disaster management systems (IDSSs) in South Korea have focused only on storms, floods, and earthquakes, and they have not used past data. This research proposes a new conceptual framework of an IDSS for disaster management, with particular attention paid to wildfires and cold/heat waves. The IDSS uses big data collected from open application programming interface (API) and artificial intelligence (AI) algorithms to help decision-makers make faster and more accurate decisions. In addition, a simple example of the use of a convolutional neural network (CNN) to detect fire in surveillance video has been developed, which can be used for automatic fire detection and provide an appropriate response. The system will also consider connecting to open source intelligence (OSINT) to identify vulnerabilities, mitigate risks, and develop more robust security policies than those currently in place to prevent cyber-attacks.

Fire disaster early detection system in residential areas

The number of residential fires in urban areas is increasing every year. The use of automatic fire detection technology can prevent greater fire disaster losses. The fire detection system works by detecting increasing degrees of heat, ultraviolet radiation by flames, and gas leaks. The system will start automation if the LM35 Temperature sensor, MQ2 Gas sensor, and Light Dependent Resistor (LDR) detect the occurrence of fire according to the initial settings on Arduino Uno. After Arduino Uno processes input data from the sensor, there will be a notification marked with the Buzzer and a text will appear on the Liquid Crystal Display (LCD) that describes the condition of the sensor reading. Arduino Uno configures data via the GSM SIM900A Module. The last process is sending short messages (SMS) from the GSM SIM900A Module to the receiver to notify the initial warning of a fire. the delay time in testing the farthest distance (100 cm) is as follows: LM35 140 second Temperature Sensor, 130 second MQ2 Gas Sensor and LDR 145 seconds.

Automatic video fire detection approach based on PJF color modeling and spatio-temporal analysis

Recently, due to the huge damage caused by fires in many countries in the world, fire detection is getting more and more interest as an increasing important issue.Nowadays, the early fire detection in video surveillance scenes is emerging as an alternative solution to overcome the shortcomings of the current inefficient sensors. In this paper, we propose a new video based-fire detection method exploiting color and motion information of fire. Our approach consists in detecting all moving regions in the scene to select then areas likely to be fire. Further, motion analysis is required to identify the accurate fire regions. The proposed method is evaluated on different video datasets containing diverse fire and non-fire videos. Experimental results demonstrate the effectiveness of our proposed method by achieving high fire detection and low false alarms rates. Moreover, it greatly outperforms the related works with 98.81 % accuracy and only 2% of false positive rate.

Unmanned Parking System using Lab view

The existing parking system requires manual support for controlling and monitoring vehicles. The motive of this system is to find out appropriate solution of parking related issues in different aspects. The proposed system can be control the vehicle parking system in shopping malls, Theatres, outdoor stadiums etc. The proposed system consists of two levels of parking for vehicles, where each level has limit of 100. In this proposed parking control system IR sensors will detect and count the number of vehicles on arrival and guided to enter into the vacant parking levels accordingly. This system is designed with automatic lighting and fire detection facility.

A Fire Detection Algorithm Based on Tchebichef Moment Invariants and PSO-SVM

Automatic fire detection, which can detect and raise the alarm for fire early, is expected to help reduce the loss of life and property as much as possible. Due to its advantages over traditional methods, image processing technology has been applied gradually in fire detection. In this paper, a novel algorithm is proposed to achieve fire image detection, combined with Tchebichef (sometimes referred to as Chebyshev) moment invariants (TMIs) and particle swarm optimization-support vector machine (PSO-SVM). According to the correlation between geometric moments and Tchebichef moments, the translation, rotation, and scaling (TRS) invariants of Tchebichef moments are obtained first. Then, the TMIs of candidate images are calculated to construct feature vectors. To gain the best detection performance, a PSO-SVM model is proposed, where the kernel parameter and penalty factor of support vector machine (SVM) are optimized by particle swarm optimization (PSO). Then, the PSO-SVM model is utilized to identify the fire images. Compared with algorithms based on Hu moment invariants (HMIs) and Zernike moment invariants (ZMIs), the experimental results show that the proposed algorithm can improve the detection accuracy, achieving the highest detection rate of 98.18%. Moreover, it still exhibits the best performance even if the size of the training sample set is small and the images are transformed by TRS.

Development of Automatic Fire Detection and Rescue System using GSM

Development of Automatic Fire Detection and Rescue System using GSM

Towards Development of a Low Cost Early Fire Detection System Using Wireless Sensor Network and Machine Vision

Fire is one of the most prominent threat to safety of human and property, in both domestic and industrial setups. Efficiently combating a fire threat, usually, depends on how early the fire is detected. This paper reports work for development of a low cost wireless sensor-based system for surveillance and early fire detection, using machine vision technique. The system consists of an on-board camera node, capable of transmitting videos over wireless network to a remote host computer that runs an image processing based fire detection algorithm. The system is standalone and portable with the capability of transmitting videos to virtually anywhere in the world. Prototype of the system has been successfully tested, performing video streaming alongwith segmentation of fire regions using HSI features of the retrieved images. Future work will inlcude automatic fire detection and alarm generation alongwith the extension of the system on multiple and widely scattered transmission nodes.

Noise Elimination Method in Automatic Fire Detection Equipment in Accordance with the Communication Distance

This research focused on the communication noise level depending on the distance for conventional fire detection systems (RS-485 codetransmitter) in comparison to the newly developed optical cable automatic fire detection system (optical communication code-transmitter. The noise levels of the RS-485 code-transmitter were respectively 10, 200, 1200, and 2400 mV for the communication distances of 0, 1, 40, and 80 m and showed a correlation between the increase of the communication noise with the increase of the communication distance. However, the optical communication code-transmitter did not show any sign of communication noise at these four different distances. Therefore, the newly developed optical communication system is an ideal system to avoid false alarms in automatic fire detection system.