scholarly journals Experimental Discussion on Fire Image Recognition Based on Feature Extraction

2021 ◽  
Vol 2066 (1) ◽  
pp. 012086
Author(s):  
Yongyi Cui ◽  
Fang Qu
Keyword(s):  
Fire Detection ◽  
Experimental Simulation ◽  
Watershed Algorithm ◽  
Simulation Environment ◽  
Basic Principles ◽  
Processing Technologies ◽  
Detection Technology ◽  
Image Area ◽  
Indoor Conditions ◽  
Fire Recognition

Abstract Video image-based fire detection technology can overcome some shortcomings of traditional fire detection, and has a good development prospect. This paper summarizes the basic principles of image-based fire detection, and analyzes the main features of fire combustion images. According to these features, firstly, the interframe difference method and the watershed algorithm are used to extract the suspected fire image area which may occur. Then, the features of flame image in early fire stage, such as increasing flame area, fluttering edge, irregular shape and flame color, are used as fire recognition criteria. Meanwhile, various image processing technologies and algorithms are used to extract the four main features of the fire, so as to eliminate various sources of interference and further determine whether a fire has occurred. Finally, a variety of different fuels were selected under indoor conditions to simulate fire experiments under different conditions, and the video was recorded. Fire recognition experiments were carried out using experimental videos and some videos found on the Internet. The experimental results show that the extraction and further recognition of suspected fire areas are both effective. However, the experimental simulation environment is relatively simple, and many theoretical and practical problems need to be further studied and solved.

scholarly journals Fire detection with infrared images using cascaded neural network

2019 ◽  
Vol 13 ◽  
pp. 174830261989543
Author(s):  
Li Deng ◽  
Qian Chen ◽  
Yuanhua He ◽  
Xiubao Sui ◽  
Quanyi Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
False Alarm ◽  
False Alarm Rate ◽  
Early Stage ◽  
Infrared Camera ◽  
Fire Detection ◽  
Image Texture ◽  
Infrared Images ◽  
Detection Technology ◽  
Civil Aircraft

The existing equipment of civil aircraft cargo fire detection mainly uses photoelectric smoke detectors, which has a high false alarm rate. According to Federal Aviation Agency’s (FAA) statistics, the false alarm rate is as high as 99%. 1 In the cargo of civil aircraft, the traditional photoelectric detection technology cannot effectively distinguish interference particles from smoke particles. Since the video smoke detection technology has proven to be reliable in many large scenarios, a deep learning method of image processing for fire detection is proposed. The proposed convolutional neural network is constructed of front end network and back end network cascaded with the capsule network and the circularity computation for the dynamic infrared fire image texture extraction. In order to accurately identify whether there is a fire in the area and give the kind of burning substances, a series of fuels are selected, such as n-heptane, cyclohexane, and carton for combustion reaction, and infrared camera is used to take infrared images of all fuel combustion. Experimental results show that the proposed method can effectively detect fire at the early stage of fire which is applicable for fire detection in civil aircraft cargoes.

Design of Outdoor Fire Intelligent Alarm System Based on Image Recognition

2019 ◽  
Vol 34 (07) ◽  
pp. 2050018
Author(s):  
Kun Zhou ◽  
Xi Zhang
Keyword(s):  
Neural Network ◽  
Image Recognition ◽  
Low Cost ◽  
Fire Detection ◽  
Human Society ◽  
Alarm System ◽  
Training Algorithms ◽  
Fire Control ◽  
Detection Technology ◽  
Control Work

Fire is one of the most common serious disasters in human society. It is a kind of burning phenomenon that is out of control in time and space. When a fire occurs, how to detect the fire quickly and remove it in the budding state has become the key content of fire control work. Outdoor fire is very common in our daily life, and once it occurs without effective and timely control, it will cause huge losses. Therefore, it is particularly important to study an intelligent alarm system for outdoor fire. Generally, fire detection technology can be divided into sensor fire detection technology and image fire detection technology. Sensor fire detection technology is low cost and easy to design, but its application field is limited. Under the interference of many factors outside, misjudgement and missed judgement will occur. Image fire detection technology can achieve certain detection function through manual design of features and classifiers, but there are still defects in the application in the actual diversified environment. With the development of neural network technology in recent years, it has made great breakthroughs in the field of image recognition. Its judgment type is obtained through a large number of data training algorithms. Because of its automatic feature extraction and classification characteristics, it can effectively adapt to the external environment. Therefore, this paper proposes an end-to-end two-stream neural network model to detect fires, uses fire video on the network to train the algorithm, and then uses the fire database to test. Compared with the existing fire detection algorithms, it is found that the proposed method has good practicability and versatility, and provides a good reference for the development of fire detection technology.

scholarly journals Early smoke detection of forest fires based on SVM image segmentation

2019 ◽  
Vol 65 (No. 4) ◽  
pp. 150-159
Author(s):  
Ding Xiong ◽  
Lu Yan
Keyword(s):  
Image Segmentation ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Support Vector ◽  
Smoke Detection ◽  
Large Space ◽  
Merging Algorithm ◽  
Detection Technology

A smoke detection method is proposed in single-frame video sequence images for forest fire detection in large space and complex scenes. A new superpixel merging algorithm is further studied to improve the existing horizon detection algorithm. This method performs Simple Linear Iterative Clustering (SLIC) superpixel segmentation on the image, and the over-segmentation problem is solved with a new superpixel merging algorithm. The improved sky horizon line segmentation algorithm is used to eliminate the interference of clouds in the sky for smoke detection. According to the spectral features, the superpixel blocks are classified by support vector machine (SVM). The experimental results show that the superpixel merging algorithm is efficient and simple, and easy to program. The smoke detection technology based on image segmentation can eliminate the interference of noise such as clouds and fog on smoke detection. The accuracy of smoke detection is 77% in a forest scene, it can be used as an auxiliary means of monitoring forest fires. A new attempt is given for forest fire warning and automatic detection.

scholarly journals Fire Detection Technology for Turbine-Powered Transportation

1970 ◽  
Author(s):  
T. M. Trumble
Keyword(s):  
High Temperature ◽  
Detection System ◽  
Fire Detection ◽  
Vehicle Design ◽  
Design Phase ◽  
Detection Systems ◽  
Detection Technology ◽  
Operational Envelope

The problems of providing a fire and overheat detection system for turbine-powered vehicles must be solved during the design phase of the vehicle. In order to accomplish this goal the vehicle design engineer must be aware of the basic constraints involved in the application of fire detection technology. This paper presents a condensed method for understanding, designing and evaluating fire and overheat detection systems. Advanced concepts and technologies such as optical redundancy and high temperature ultraviolet sensors are discussed. Performance of fire and overheat detection systems designed using this approach will provide maximum safety for those using the vehicles, as well as those in its operational envelope.

Studies on CUI of Carbon Steel in Simulation Environment Containing Chloride and Sulfur at 80°C

2012 ◽  
Vol 509 ◽  
pp. 166-170
Author(s):  
Jian Qun Tang ◽  
Jian Ming Gong ◽  
Ying Jie Jiang
Keyword(s):  
Carbon Steel ◽  
Metal Surface ◽  
Localized Corrosion ◽  
Experimental Simulation ◽  
Simulation Environment ◽  
Nacl Concentration ◽  
Corrosion Behaviors ◽  
Small Cracks ◽  
Testing Solution ◽  
Corrosion Under Insulation

Corrosion under insulation (CUI) is a localized corrosion occurring at the interface of a metal surface and the insulation on the metal surface. In order to explore the mechanism of CUI, some tests were conducted on 20 # carbon steel under insulation dripping different testing solution at 80°C in an experimental simulation device for CUI. Corrosion behaviors were analyzed by weight loss methods and other methods. The results showed that CUI rate of 20# carbon steel increased with NaCl concentration. The addition of sulfur and the decrease of pH promoted corrosion. The pits and small cracks were found on the corroded metal and the bonding between products and matrix was strong.

Design and Implementation of Digital Sensor Simulator

2013 ◽  
Vol 411-414 ◽  
pp. 1581-1587
Author(s):  
Gai Fang Wang ◽  
Feng Feng Fan ◽  
Xi Tao Xing ◽  
Yong Wang
Keyword(s):  
Rapid Development ◽  
General Structure ◽  
Sensor Technology ◽  
Simulation Environment ◽  
Sensor Applications ◽  
Detection Technology ◽  
Additional Costs ◽  
High Level ◽  
Performance Results ◽  
Short Time

With the rapid development of sensor technology recently, sensors have been applied to various fields for detecting object states, e.g. intelligent agriculture, intelligent power, intelligent city, the Internet of Things, etc., and have becoming more and more critical for dynamic data acquisition. Due to detection environment, detection technology, costs and other factors, access to actual sensors for developing or debugging a sensor application may cause additional costs and time. Meanwhile, testing new sensor applications and protocols needs appropriate feasible ways with low costs and short time. Therefore, it is fairly urgent for designing and developing a simulation environment of sensors and sensor applications. This paper parsed the general structure of digital sensors, and then designed domain-based high level architecture of digital sensor simulator. Finally, the prototype of digital sensor simulator was developed and demonstrated the proper performance. Results show that digital sensor simulator would provide an effective way for testing novel sensors and protocols and also play an important role for constituting sensor network simulation environment.

Fiber Optic Temperature and Gas Concentration Sensor for Mine Goaf Area Inner Detecting

2012 ◽  
Vol 524-527 ◽  
pp. 302-305
Author(s):  
Yu Bin Wei ◽  
Xu You Wang ◽  
Min Xin ◽  
Tong Yu Liu ◽  
Chang Wang
Keyword(s):  
Spontaneous Combustion ◽  
Detection System ◽  
Fire Detection ◽  
Tunable Diode Laser ◽  
Gas Concentration ◽  
Beam Tube ◽  
Sensing Technology ◽  
Detection Technology ◽  
Primary Means ◽  
On Line

Spontaneous combustion in coal goaf area is one of major disasters in coal mines. Detection technology based on signature Gas and Temperature is the primary means of spontaneous combustion forecasting of coal goaf area. A real-time remote fire detection system is proposed based on tunable diode laser absorption spectroscopy technology and FBG temperature sensing technology, to achieve valid detect of gas concentration and temperature. The System include fiber mathen concentration sensor and fiber temperature sensor based FBG. The system achieved remote on-line monitoring of gas concentration and temperature in mine goaf, meet the fire forecast need for Coal goaf area. There are obvious advantages Compared with the existing beam tube monitoring system in coal mine.

Fire Detection Technology Based on Infrared Image Processing

2013 ◽  
Vol 347-350 ◽  
pp. 3426-3430 ◽  
Author(s):  
Xiao Jun Liu
Keyword(s):  
Genetic Algorithm ◽  
Infrared Image ◽  
Fire Detection ◽  
Ccd Camera ◽  
Chain Code ◽  
Light Interference ◽  
Feature Change ◽  
Infrared Image Processing ◽  
Detection Technology ◽  
Background Difference

This paper proposes a method to detect fire by processing the images captured by an CCD camera with infrared filter. First, the flame objects are detected by using two consecutive frames difference and background difference. Using genetic algorithm to optimize the threshold, the image is segmented by using Ostu. The boundary chain code is acquired on the basis of extracting flame contour. Lastly, shape feature, change feature and edge jitter feature are used to judge whether the fire exists. This method suppresses visible light interference. The experiment results show that the algorithm has higher reorganization rate in different environment.

scholarly journals Hardware Module Design and Software Implementation of Multisensor Fire Detection and Notification System Using Fuzzy Logic and Convolutional Neural Networks (CNNs)

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Robert A. Sowah ◽  
Kwaku Apeadu ◽  
Francis Gatsi ◽  
Kwame O. Ampadu ◽  
Baffour S. Mensah
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Convolutional Neural Networks ◽  
Fire Detection ◽  
Web Based ◽  
Notification System ◽  
Multisensor Data Fusion ◽  
Standard Fire ◽  
Area Of Interest ◽  
Detection Technology

This paper presents the design and development of a fuzzy logic-based multisensor fire detection and a web-based notification system with trained convolutional neural networks for both proximity and wide-area fire detection. Until recently, most consumer-grade fire detection systems relied solely on smoke detectors. These offer limited protection due to the type of fire present and the detection technology at use. To solve this problem, we present a multisensor data fusion with convolutional neural network (CNN) fire detection and notification technology. Convolutional Neural Networks are mainstream methods of deep learning due to their ability to perform feature extraction and classification in the same architecture. The system is designed to enable early detection of fire in residential, commercial, and industrial environments by using multiple fire signatures such as flames, smoke, and heat. The incorporation of the convolutional neural networks enables broader coverage of the area of interest, using visuals from surveillance cameras. With access granted to the web-based system, the fire and rescue crew gets notified in real-time with location information. The efficiency of the fire detection and notification system employed by standard fire detectors and the multisensor remote-based notification approach adopted in this paper showed significant improvements with timely fire detection, alerting, and response time for firefighting. The final experimental and performance evaluation results showed that the accuracy rate of CNN was 94% and that of the fuzzy logic unit is 90%.

Spectral Spatio-Temporal Fire Model for Video Fire Detection

2018 ◽  
Vol 32 (05) ◽  
pp. 1850013 ◽  
Author(s):  
Zhaohui Wu ◽  
Tao Song ◽  
Xiaobo Wu ◽  
Xuqiang Shao ◽  
Yan Liu
Keyword(s):  
Color Space ◽  
Fire Detection ◽  
Svm Classifier ◽  
False Alarms ◽  
Surveillance Video ◽  
Detection Systems ◽  
Main Challenge ◽  
Detection Technology ◽  
Spatio Temporal ◽  
Candidate Regions

Fire detection technology aroused people’s attention increasingly. The main challenge of the fire detection systems is how to reduce false alarms caused by objects like fire’s colors. Most existing algorithms used only features of fire in visual field. In this work, we put forward a new algorithm to detect dynamic fire from the surveillance video based on the combination of radiation domain features model. First, a fire color model is used to extract flame-like pixels as candidate areas in YCbCr space. Second, we convert the candidate regions from the traditional color space into radiation domain in advance by camera calibration. And we use seven features to model the spectral spatio-temporal model of the fire to more accurately characterize the physical and optical properties of the fire. Finally, we choose a two-class SVM classifier to identify the fire from the candidate areas and use a radial basis function kernel to improve the accuracy of the recognition. Two different sets of data are used to validate the algorithm we proposed. And the experimental results indicate that our method performs well in video fire surveillance.

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