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 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.

scholarly journals Ferrography Wear Particles Image Recognition Based on Extreme Learning Machine

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Qiong Li ◽  
Tingting Zhao ◽  
Lingchao Zhang ◽  
Wenhui Sun ◽  
Xi Zhao
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Image Recognition ◽  
Rapid Development ◽  
Texture Features ◽  
Wear Particle ◽  
Training Algorithms ◽  
Wear Particles ◽  
Learning Machine ◽  
Elm Classifier

The morphology of wear particles reflects the complex properties of wear processes involved in particle formation. Typically, the morphology of wear particles is evaluated qualitatively based on microscopy observations. This procedure relies upon the experts’ knowledge and, thus, is not always objective and cheap. With the rapid development of computer image processing technology, neural network based on traditional gradient training algorithm can be used to recognize them. However, the feedforward neural network based on traditional gradient training algorithms for image segmentation creates many issues, such as needing multiple iterations to converge and easy fall into local minimum, which restrict its development heavily. Recently, extreme learning machine (ELM) for single-hidden-layer feedforward neural networks (SLFN) has been attracting attentions for its faster learning speed and better generalization performance than those of traditional gradient-based learning algorithms. In this paper, we propose to employ ELM for ferrography wear particles image recognition. We extract the shape features, color features, and texture features of five typical kinds of wear particles as the input of the ELM classifier and set five types of wear particles as the output of the ELM classifier. Therefore, the novel ferrography wear particle classifier is founded based on ELM.

scholarly journals A Low-cost Artificial Neural Network Model for Raspberry Pi

2020 ◽  
Vol 10 (2) ◽  
pp. 5466-5469 ◽  
Author(s):  
S. N. Truong
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Embedded System ◽  
Real Time ◽  
Image Recognition ◽  
Low Cost ◽  
Recognition Rate ◽  
Raspberry Pi ◽  
The Neural Network ◽  
Binary Array

In this paper, a ternary neural network with complementary binary arrays is proposed for representing the signed synaptic weights. The proposed ternary neural network is deployed on a low-cost Raspberry Pi board embedded system for the application of speech and image recognition. In conventional neural networks, the signed synaptic weights of –1, 0, and 1 are represented by 8-bit integers. To reduce the amount of required memory for signed synaptic weights, the signed values were represented by a complementary binary array. For the binary inputs, the multiplication of two binary numbers is replaced by the bit-wise AND operation to speed up the performance of the neural network. Regarding image recognition, the MINST dataset was used for training and testing of the proposed neural network. The recognition rate was as high as 94%. The proposed ternary neural network was applied to real-time object recognition. The recognition rate for recognizing 10 simple objects captured from the camera was 89%. The proposed ternary neural network with the complementary binary array for representing the signed synaptic weights can reduce the required memory for storing the model’s parameters and internal parameters by 75%. The proposed ternary neural network is 4.2, 2.7, and 2.4 times faster than the conventional ternary neural network for MNIST image recognition, speech commands recognition, and real-time object recognition respectively.

Information Fusion Technology Based on Wireless Fire Detection and Alarm System

2013 ◽  
Vol 846-847 ◽  
pp. 883-887
Author(s):  
Ling Hui Niu ◽  
Zhu Ge Hu
Keyword(s):  
Wireless Communication ◽  
Communication Technology ◽  
Information Fusion ◽  
Fire Detection ◽  
Alarm System ◽  
Chemical Signal ◽  
Fusion Technology ◽  
Fire Alarm ◽  
Detection Technology ◽  
Fire Alarm System

In order to solve the problem, which is "the traditional fire alarm system is only used to detect a particular physical or chemical signal of the fire, moreover, false alarm and failure alarm can occur easily. We apply multi-sensor composite detection technology and wireless communication technology in the fire detection and alarm system,and design an intelligence distributed wireless fire detection alarm system, which is mainly based on STM32 control chip.

scholarly journals Experimental Discussion on Fire Image Recognition Based on Deep Learning

2021 ◽  
Vol 2066 (1) ◽  
pp. 012071
Author(s):  
Yongyi Cui ◽  
Fang Qu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Rapid Development ◽  
Fire Detection ◽  
Learning Technology ◽  
Fire Recognition

Abstract Fire detection technology based on video images is an emerging technology that has its own unique advantages in many aspects. With the rapid development of deep learning technology, Convolutional Neural Networks based on deep learning theory show unique advantages in many image recognition fields. This paper uses Convolutional Neural Networks to try to identify fire in video surveillance images. This paper introduces the main processing flow of Convolutional Neural Networks when completing image recognition tasks, and elaborates the basic principles and ideas of each stage of image recognition in detail. The Pytorch deep learning framework is used to build a Convolutional Neural Network for training, verification and testing for fire recognition. In view of the lack of a standard and authoritative fire recognition training set, we have conducted experiments on fires with various interference sources under various environmental conditions using a variety of fuels in the laboratory, and recorded videos. Finally, the Convolutional Neural Network was trained, verified and tested by using experimental videos, fire videos on the Internet as well as other interference source videos that may be misjudged as fires.

Research of Multi-Sensor Information Fusion Fire Detection System

2013 ◽  
Vol 860-863 ◽  
pp. 2745-2749
Author(s):  
Yan Lei Jiang
Keyword(s):  
Neural Network ◽  
Information Fusion ◽  
Fuzzy Neural Network ◽  
Detection System ◽  
Fire Detection ◽  
Threshold Value ◽  
Sensor Data ◽  
Detection Technology ◽  
Fuzzy Neural ◽  
Fire Detection System

To reduce false fire alarms, combining with the character of fire signal, a kind of intelligent fire detection system of multi-sensor information fusion based on fuzzy neural network is proposed in this paper . This fire detector fuses three sensor data including temperature, smoke and CO air which have obvious character in fire and fire probability can be obtained by intelligent arithmetic of fuzzy neural network. As a result, The accuracy of the fire detection is improved effectively and the feasibility and validity of the system are proved by the simulation effects. 0 Foreword The purpose of fire detection technology is to make accurate judgments of the fire and to predict the fire in the early time, so that people's lives and property can be protected. Based on the monitoring of physical phenomena such as light, smoke, heat, the traditional fire detection usually monitors one kind of physical quantity and establishes a certain threshold value as the criterion for the fire. In practice, it is discovered that fire monitoring, based on a certain physical quantity and threshold value, is often inevitably influenced by a certain similar environmental factors influence which causes false alarm. 1 Multi-sensor Data Fusion Fire Detection System For any kind of detective object, using only one kind of information to reflect its condition is not complete. Only through getting, integrating and using various multi-dimensional information of the same object, it can detect the fire accurately and early. In view of the fact that unit fire detection technology has been unable to meet the needs of real fire alarm, the system uses multiple information fusion fire detection, which is not the simple combination of the fire detectors original single parameter, but the implementation of multiple simultaneous detection, extraction of useful and accurate information. According to different types of fire parameters, it applies intelligent algorithms, fuses the fire parameters of multi-sensor fusion, and determines whether there is a fire hazard. It overcomes the limitations of a single sensor, and effectively improves the ability of identifying real or false fires. Under normal circumstances, CO is extremely low in the air. Only by burning massive CO can be produced, which causes the density of CO in the air to increase sharply. Thus the detection of CO gas will be in large part reflects whether the combustion phenomenon happens or not. The occurring of fire is often accompanied with the elevation of temperature and the enlargement of smoke density, so the system of fire detectors uses 3-layer structure of multi-sensor fusion, selects temperature sensors, smoke sensors, gas sensors, the temperature signal, smoke concentration and the CO concentration as the fire detection signal. 2 Fuzzy Neural Network Applying fuzzy neural network to fire detection information processing can greatly improve the timeliness and accuracy of fire detection, and reduce the rate of false alarm.This system uses fuzzy neural network as shown in Figure 1. Before and after the neural network in the system is in series with the fuzzy system, in order to facilitate the procession of neural network, the smog density signal from the environment examination, the temperature signal as well as the gas signal through the signal pretreatment should be normalized, and sends these three normalized values into the fuzzy system, uses trigonometric functions for transformation, and obtains three degree of membership and the feedback signal of neural network as the neural network input.

Low-Cost Hardware Implementation of Human Blood Identification Device Using Feed-Forward Artificial Neural Network on FPGA

2018 ◽  
Author(s):  
Rizki Eka Putri ◽  
Denny Darlis
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Low Cost ◽  
Image Resolution ◽  
Blood Type ◽  
Feed Forward ◽  
Testing Facility ◽  
System Input ◽  
Artificial Neural ◽  
Short Time

This article was under review for ICELTICS 2018 -- In the medical world there is still service dissatisfaction caused by lack of blood type testing facility. If the number of tested blood arise, a lot of problems will occur so that electronic devices are needed to determine the blood type accurately and in short time. In this research we implemented an Artificial Neural Network on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board to identify the blood type. This research uses blood sample image as system input. VHSIC Hardware Discription Language is the language to describe the algorithm. The algorithm used is feed-forward propagation of backpropagation neural network. There are 3 layers used in design, they are input, hidden1, and output. At hidden1layer has two neurons. In this study the accuracy of detection obtained are 92%, 92%, 92%, 90% and 86% for 32x32, 48x48, 64x64, 80x80, and 96x96 pixel blood image resolution, respectively.

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