New methods for reducing the number of false alarms in fire detection systems

1994 ◽  
Vol 30 (2) ◽  
pp. 250-268 ◽  
Author(s):  
M. Thuillard
Keyword(s):  
Fire Detection ◽  
False Alarms ◽  
Detection Systems ◽  
New Methods ◽  
Number Of False Alarms ◽  
In Fire

scholarly journals New functions of multicriteria ship fire alarm systems

2021 ◽  
pp. 118-123
Author(s):  
В.Н. Круглеевский ◽  
В.В. Вислогузов ◽  
A.A. Таранцев ◽  
С.Н. Турусов
Keyword(s):  
Fire Detection ◽  
Electrical Cable ◽  
Comparative Tests ◽  
Fire Alarm ◽  
Alarm Signals ◽  
Detection Systems ◽  
Alarm Systems ◽  
Characteristic Features ◽  
In Fire ◽  
Marine Vessels

В настоящей статье рассматриваются вопросы развития пожарных извещателей, контролирующих появление дыма, превышение заданного значения температуры и скорости ее роста, наличие угарного газа и использующих мультикритериальные алгоритмы для оценки обоснованности сигналов тревоги. Анализируются результаты проведенных отечественными организациями сравнительных испытаний мультикритериальных и традиционных «пороговых» пожарных извещателей и возможности их применения на судах в составе систем пожарной сигнализации. Определено, что при повторении одних и тех же модельных очагов пожаров зафиксированные значения контролируемых параметров отличались незначительно. При этом для каждого модельного очага можно было обнаружить свои характерные черты. Сделан вывод о том, что внедрение мультикритериальных алгоритмов обработки информации в судовые системы обнаружения пожаров не только сокращает время обнаружения пожара, но и позволяет расширить функциональные возможности системы. Используя мультикритериальные пожарные извещатели в системах пожарной сигнализации можно будет распознавать, что именно горит: дизельное топливо, ветошь, изоляция электрического кабеля или что-либо другое. Отмечается, что требования к судовым мультикритериальным системам сигнализации обнаружения пожара нашли свое отражение в Правилах классификации и постройки морских судов Российского морского регистра судоходства. This article discusses the development of fire detectors that control the appearance of smoke, the excess of a given temperature and the rate of its growth, the presence of carbon monoxide and use multicriteria algorithms to assess the validity of alarm signals. The results of comparative tests of multicriteria and traditional fire detectors conducted by domestic organizations and the possibility of their use on ships as part of fire alarm systems are analyzed. It was determined that when the same model fires were repeated, the recorded values of the controlled parameters differed slightly. At the same time, for each model focus, it was possible to detect its own characteristic features. It is concluded that the introduction of multicriteria algorithms for information processing in ship fire detection systems not only reduces the time of fire detection, but also allows you to expand the functionality of the system. Using multi-criteria fire detectors in fire alarm systems,it will be possible to recognize what exactly is burning: diesel fuel, rags, electrical cable insulation, or anything else. It is noted that the requirements for ship multicriteria fire detection alarm systems are reflected in the Rules for the Classification and Construction of Marine Vessels of the Russian Maritime Register of Shipping.

Method for oil balance control under quasi-stationary modes of operation of an oil pipeline section with free flow

2021 ◽  
pp. 40-45
Author(s):  
Рустам Зайтунович Сунагатуллин ◽  
Антон Михайлович Чионов ◽  
Семен Васильевич Петренко
Keyword(s):  
Balance Control ◽  
Operation Mode ◽  
Rate Of Change ◽  
Free Flow ◽  
False Alarms ◽  
Oil Pipeline ◽  
Detection Systems ◽  
Pipeline Section ◽  
Number Of False Alarms ◽  
Oil Flow

Автоматизированные системы управления используются в нефтепроводном транспорте с целью автоматизации технологических процессов транспортировки нефти и нефтепродуктов, при этом основной задачей является обеспечение надежности и безопасности перекачки, что невозможно без контроля целостности трубопровода. В связи с этим актуальной остается тема обнаружения утечек, требуют продолжения исследования в области повышения надежности автоматизированных систем обнаружения утечек (СОУ). При эксплуатации СОУ особую важность представляет описание процессов заполнения и опорожнения участков трубопровода с безнапорным течением. Скорость установления стационарного режима работы таких участков и участков с полным сечением существенно отличается. Слабые возмущения давления могут приводить к значительному дебалансу расхода нефти и, как следствие, вызывать ложные срабатывания СОУ. Авторами представлен алгоритм вычисления скорости изменения запаса нефти на участке трубопровода при медленном изменении размера самотечной полости, на основании которого предложен способ корректировки уравнения баланса вещества. Показано использование разработанного алгоритма для повышения чувствительности СОУ и уменьшения количества ложных срабатываний. During the operation of leak detection systems (LDS), it is of great importance to describe the processes of filling and emptying pipeline free flow sections. The speed of establishing a stationary operation mode of such sections and full sections is significantly different. Weak pressure perturbations can lead to significant imbalance in the oil flow rate and, as a consequence, cause false LDS positives. The authors present an algorithm for calculating rate of change in oil reserve in the pipeline section with a slow change in the size of gravity cavity, on the basis of which a method for adjusting the substance balance equation is proposed. The use of a developed algorithm is shown to increase the sensitivity of LDS and reduce the number of false alarms.

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

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1241
Author(s):  
Yakhyokhuja Valikhujaev ◽  
Akmalbek Abdusalomov ◽  
Young Im Cho
Keyword(s):  
Cost Effective ◽  
Classification Performance ◽  
Detection Methods ◽  
False Alarms ◽  
Surveillance Systems ◽  
Smoke Detection ◽  
Detection Systems ◽  
Number Of False Alarms ◽  
Unseen Data ◽  
Vision Detection

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.

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.

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.

An approach for the compensation of the background variability in fire detection systems

2001 ◽  
Vol 42 (1) ◽  
pp. 23-30 ◽  
Author(s):  
M.F. Ugarte ◽  
R.I. Zequeira ◽  
F. López
Keyword(s):  
Fire Detection ◽  
Detection Systems ◽  
In Fire

scholarly journals Adaptive Quasi-Unsupervised Detection of Smoke Plume by LiDAR

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6602
Author(s):  
Riccardo Rossi ◽  
Michela Gelfusa ◽  
Andrea Malizia ◽  
Pasqualino Gaudio
Keyword(s):  
Fire Detection ◽  
Local Variation ◽  
Combustion Products ◽  
False Alarms ◽  
Analysis Method ◽  
Smoke Plume ◽  
Number Of False Alarms ◽  
Experimental Apparatus ◽  
Unsupervised Approach

The early detection of fire is one of the possible applications of LiDAR techniques. The smoke generated by a fire is mainly compounded of CO2, H2O, particulate, and other combustion products, which involve the local variation of the scattering of the electromagnetic wave at specific wavelengths. The increases of the backscattering coefficient are transduced in peaks on the signal of the backscattering power recorded by the LiDAR system, located exactly where the smoke plume is, allowing not only the detection of a fire but also its localization. The signal processing of the LiDAR signals is critical in the determination of the performances of the fire detection. It is important that the sensitivity of the apparatus is high enough but also that the number of false alarms is small, in order to avoid the trigger of useless and expensive countermeasures. In this work, a new analysis method, based on an adaptive quasi-unsupervised approach was used to ensure that the algorithm is continuously updated to the boundary conditions of the system, such as the weather and experimental apparatus issues. The method has been tested on an experimental campaign of 227 pulses and the performances have been analyzed in terms of sensitivity and specificity.

Analysis of false alarms given by automatic fire detection systems

1985 ◽  
Vol 13 (3) ◽  
pp. 163-174 ◽  
Author(s):  
Yash Gupta ◽  
Avinash Dharmadhikari
Keyword(s):  
Fire Detection ◽  
False Alarms ◽  
Detection Systems

scholarly journals Three Ways to Improve the Performance of Real-Life Camera-Based Fall Detection Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Glen Debard ◽  
Marc Mertens ◽  
Toon Goedemé ◽  
Tinne Tuytelaars ◽  
Bart Vanrumste
Keyword(s):  
False Alarm ◽  
Detection System ◽  
Real Life ◽  
Fall Detection ◽  
Training Data ◽  
False Alarms ◽  
Detection Systems ◽  
Number Of False Alarms ◽  
Real Life Data ◽  
Precision Recall Curve

More than thirty percent of persons over 65 years fall at least once a year and are often not able to get up again. Camera-based fall detection systems can help by triggering an alarm when falls occur. Previously we showed that real-life data poses significant challenges, resulting in high false alarm rates. Here, we show three ways to tackle this. First, using a particle filter combined with a person detector increases the robustness of our foreground segmentation, reducing the number of false alarms by 50%. Second, selecting only nonoccluded falls for training further decreases the false alarm rate on average from 31.4 to 26 falls per day. But, most importantly, this improvement is also shown by the doubling of the AUC of the precision-recall curve compared to using all falls. Third, personalizing the detector by adding several days containing only normal activities, no fall incidents, of the monitored person to the training data further increases the robustness of our fall detection system. In one case, this reduced the number of false alarms by a factor of 7 while in another one the sensitivity increased by 17% for an increase of the false alarms of 11%.

scholarly journals Decision algorithms in fire detection systems

2011 ◽  
Vol 8 (2) ◽  
pp. 155-161 ◽  
Author(s):  
Jovan Ristic ◽  
Dragana Radosavljevic
Keyword(s):  
False Alarm ◽  
Fire Detection ◽  
Working Time ◽  
Detection Systems ◽  
In Fire ◽  
Decision Algorithms

Analogue (and addressable) fire detection systems enables a new quality in improving sensitivity to real fires and reducing susceptibility to nuisance alarm sources. Different decision algorithms types were developed with intention to improve sensitivity and reduce false alarm occurrence. At the beginning, it was free alarm level adjustment based on preset level. Majority of multi-criteria decision work was based on multi-sensor (multi-signature) decision algorithms - using different type of sensors on the same location or, rather, using different aspects (level and rise) of one sensor measured value. Our idea is to improve sensitivity and reduce false alarm occurrence by forming groups of sensors that work in similar conditions (same world side in the building, same or similar technology or working time). Original multi-criteria decision algorithms based on level, rise and difference of level and rise from group average are discussed in this paper.

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