A new color space based on K-medoids clustering for fire detection

Pixel color has proven to be a useful and robust cue for detection of most objects of interest like fire. In this paper, a hybrid intelligent algorithm is proposed to detect fire pixels in the background of an image. The proposed algorithm is introduced by the combination of a computational search method based on a swarm intelligence technique and the Kemdoids clustering method in order to form a Fire-based Color Space (FCS), in fact, the new technique converts RGB color system to FCS through a 3*3 matrix. This algorithm consists of five main stages:(1) extracting fire and non-fire pixels manually from the original image. (2) using K-medoids clustering to find a Cost function to minimize the error value. (3) applying Particle Swarm Optimization (PSO) to search and find the best W components in order to minimize the fitness function. (4) reporting the best matrix including feature weights, and utilizing this matrix to convert the all original images in the database to the new color space. (5) using Otsu threshold technique to binarize the final images. As compared with some state-of-the-art techniques, the experimental results show the ability and efficiency of the new method to detect fire pixels in color images.

Local data fusion algorithm for fire detection through mobile robot

Multisensor data fusion is a technique that combines the readings of multiple sensors to detect some phenomenon. Data fusion applications are numerous and they can be used in smart buildings, environment monitoring, industry and defense applications. The main goal of multisensor data fusion is to minimize false alarms and maximize the probability of detection based on the detection of multiple sensors. In this paper a local data fusion algorithm based on luminosity, temperature and flame for fire detection is presented. The data fusion approach was embedded in a low cost mobile robot. The prototype test validation has indicated that our approach can detect fire occurrence. Moreover, the low cost project allow the development of robots that could be discarded in their fire detection missions. © 2013 IEEE.

Evaluation of filter flammability and filter bank fire detection systems /

"U.S. Atomic Energy Commission Contract AT(29-1)-1106."

The FIREDASS (Fire Detection and Suppression Simulation) Model

The FIREDASS (FIRE Detection And Suppression Simulation) project is concerned with the development of fine water mist systems as a possible replacement for the halon fire suppression system currently used in aircraft cargo holds. The project is funded by the European Commission, under the BRITE EURAM programme. The FIREDASS consortium is made up of a combination of Industrial, Academic, Research and Regulatory partners. As part of this programme of work, a computational model has been developed to help engineers optimise the design of the water mist suppression system. This computational model is based on Computational Fluid Dynamics (CFD) and is composed of the following components: fire model; mist model; two-phase radiation model; suppression model and detector/activation model. The fire model - developed by the University of Greenwich - uses prescribed release rates for heat and gaseous combustion products to represent the fire load. Typical release rates have been determined through experimentation conducted by SINTEF. The mist model - developed by the University of Greenwich - is a Lagrangian particle tracking procedure that is fully coupled to both the gas phase and the radiation field. The radiation model - developed by the National Technical University of Athens - is described using a six-flux radiation model. The suppression model - developed by SINTEF and the University of Greenwich - is based on an extinguishment crietrion that relies on oxygen concentration and temperature. The detector/ activation model - developed by Cerberus - allows the configuration of many different detector and mist configurations to be tested within the computational model. These sub-models have been integrated by the University of Greenwich into the FIREDASS software package. The model has been validated using data from the SINTEF/GEC test campaigns and it has been found that the computational model gives good agreement with these experimental results. The best agreement is obtained at the ceiling which is where the detectors and misting nozzles would be located in a real system. In this paper the model is briefly described and some results from the validation of the fire and mist model are presented.

Evaluating the SEVIRI Fire Thermal Anomaly Detection Algorithm across the Central African Republic Using the MODIS Active Fire Product

Satellite-based remote sensing of active fires is the only practical way to consistently and continuously monitor diurnal fluctuations in biomass burning from regional, to continental, to global scales. Failure to understand, quantify, and communicate the performance of an active fire detection algorithm, however, can lead to improper interpretations of the spatiotemporal distribution of biomass burning, and flawed estimates of fuel consumption and trace gas and aerosol emissions. This work evaluates the performance of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) Fire Thermal Anomaly (FTA) detection algorithm using seven months of active fire pixels detected by the Moderate Resolution Imaging Spectroradiometer (MODIS) across the Central African Republic (CAR). Results indicate that the omission rate of the SEVIRI FTA detection algorithm relative to MODIS varies spatially across the CAR, ranging from 25% in the south to 74% in the east. In the absence of confounding artifacts such as sunglint, uncertainties in the background thermal characterization, and cloud cover, the regional variation in SEVIRI's omission rate can be attributed to a coupling between SEVIRI's low spatial resolution detection bias (i.e., the inability to detect fires below a certain size and intensity) and a strong geographic gradient in active fire characteristics across the CAR. SEVIRI's commission rate relative to MODIS increases from 9% when evaluated near MODIS nadir to 53% near the MODIS scene edges, indicating that SEVIRI errors of commission at the MODIS scene edges may not be false alarms but rather true fires that MODIS failed to detect as a result of larger pixel sizes at extreme MODIS scan angles. Results from this work are expected to facilitate (i) future improvements to the SEVIRI FTA detection algorithm; (ii) the assimilation of the SEVIRI and MODIS active fire products; and (iii) the potential inclusion of SEVIRI into a network of geostationary sensors designed to achieve global diurnal active fire monitoring.

An efficient hybrid algorithm for fire flame detection

Proposing efficient methods for fire protection is becoming more and more important, because a small flame of fire may cause huge problems in social safety. In this paper, an effective fire flame detection method is investigated. This fire detection method includes four main stages: in the first step, a linear transformation is applied to convert red, green, and blue (RGB) color space through a 3∗3 matrix to a new color space. In the next step, fuzzy c-mean clustering method (FCM) is used to distinguish between fire flame and non-fire flame pixels. Particle Swarm Optimization algorithm (PSO) is also utilized in the last step to decrease the error value measured by FCM after conversion. Finally, we apply Otsu threshold method to the new converted images to make a binary picture. Empirical results show the strength, accuracy and fast-response of the proposed algorithm in detecting fire flames in color images.

Automatic wildfire detection and simulation using optical information from unmanned aerial systems

In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material. This image recognition algorithm is designed to cope with environmental occlusions such as shadows, smoke and obstructions. Once the fire is identified and classified, it is used to initialize a spatial/temporal fire simulation. This simulation is based on occupancy maps whose fidelity can be varied to include stochastic elements, various types of vegetation, weather conditions, and unique terrain. The simulations can be used to predict the effects of optimized firefighting methods to prevent the future propagation of the fires and greatly reduce time to detection of wildfires, thereby greatly minimizing the ensuing damage. This paper also documents experimental flight tests using a SenseFly Swinglet UAS conducted in Brisbane, Australia as well as modifications for custom UAS.

The development of a CFD based simulator for water mist suppression systems: the development of the fire submodel

The FIRE Detection and Suppression Simulation (FIREDASS) project was concerned with the development of water misting systems as a possible replacement for halon based fire suppression systems currently used in aircraft cargo holds and ship engine rooms. As part of this program of work, a computational model was developed to assist engineers optimize the design of water mist suppression systems. The model is based on Computational Fluid Dynamics (CFD) and comprised of the following components: fire model; mist model; two-phase radiation model; suppression model; detector/activation model. In this paper the FIREDASS software package is described and the theory behind the fire and radiation sub-models is detailed. The fire model uses prescribed release rates for heat and gaseous combustion products to represent the fire load. Typical release rates have been determined through experimentation. The radiation model is a six-flux model coupled to the gas (and mist) phase. As part of the FIREDASS project, a detailed series of fire experiments were conducted in order to validate the fire model. Model predictions are compared with data from these experiments and good agreement is found.

Complex responses of birds to landscape-level fire extent, fire severity and environmental drivers

Aim: To quantify bird responses to a large unplanned fire, taking into consideration landscape-level fire severity and extent, pre-fire site detection frequency and environmental gradients. Location: South-eastern Australia. Methods: A major wildfire in 2009 coincided with a long-term study of birds and provided a rare opportunity to quantify bird responses to wildfire. Using hierarchical Bayesian analysis, we modelled bird species richness and the detection frequency of individual species in response to a suite of explanatory variables, including (1) landscape-level fire severity and extent (2) pre-fire detection frequency, (3) site-level vegetation density and (4) environmental variables (e.g. elevation and topography). Results: Landscape-level fire severity had strong effects on bird species richness and the detection frequency of the majority of bird species. These effects varied markedly between species; most responded negatively to amount of severely burned forest in the landscape, one negatively to the amount of moderately burned forest and one responded negatively to the total area of burned forest. Only one species - the Flame Robin - responded positively to the amount of burned forest. Relationships with landscape-scale fire extent changed over time for one species - the Brown Thornbill - with initially depressed rates of detection recovering after just 2 years. The majority of species were significantly more likely to be detected in burned areas if they have been recorded there prior to the fire. Main conclusions: Birds responded strongly to the severity and spatial extent of fire. They also exhibited strong site fidelity even after severe wildfire which causes profound changes in vegetation cover - a response likely influenced by environmental features such as elevation and topography.

Combustion products of bulk aluminum rods burning in high pressure oxygen

Characterization of the combustion products released during the burning of commonly used engineering metallic materials may aid in material selection and risk assessment for the design of oxygen systems. The characterization of combustion products in regards to size distribution and morphology gives useful information for systems addressing fire detection. Aluminum rods (3.2-mm diameter cylinders) were vertically mounted inside a combustion chamber and ignited in pressurized oxygen by resistively heating an aluminum/palladium igniter wire attached to the bottom of the test sample. This paper describes the experimental work conducted to establish the particle size distribution and morphology of the resultant combustion products collected after the burning was completed and subsequently analyzed. In general, the combustion products consisted of a re-solidified oxidized slag and many small hollow spheres of size ranging from about 500 nm to 1000 µm in diameter, surfaced with quenched dendritic and grain-like structures. The combustion products were characterized using optical and scanning electron microscopy.

嵌入式智能图像处理在火灾预警系统的应用

传统的火灾检测方法一般采用感烟、感温、感光探测器等进行探测。本文提出了一种嵌入式基于图像视觉特征的火灾检测方法,以TI公司的数字多媒体处理器TMS320DM642为核心,设计实现智能前端火灾探测与自动报警系统。通过DM642对视频图像进行采集并结合相应的智能图像处理与模式识别算法,对森林火险进行实时监控。实验结果表明,该系统比传统系统更进一步减少了误报率且具有响应快、监控范围广等优点。

Prevenção e detecção de incêndios

Os fogos florestais, embora possam ser encarados como um fenómeno natural, constituem igualmente uma ameaça com impactos negativos a vários níveis. A alteração dos regimes naturais do fogo por acção antrópica tem vindo a maximizar o potencial catastrófico do fenómeno em diferentes regiões do planeta, destacando-se, em particular, a Região Mediterrânica, onde o fogo constitui uma perturbação recorrente associada, por um lado, às características climáticas desta região e, por outro, às práticas tradicionais decorrentes da presença humana nessa área, numa extensão temporal de milhares de anos. Em Portugal, o problema dos fogos florestais assume uma expressividade de particular relevo, colocando-o entre os países do sul da Europa mais atingidos pelo fogo a cada Verão. Reconhecendo o papel das condições meteorológicas nas interacções envolvidas no processo de deflagração e propagação de incêndios, a identificação de condições meteorológicas propícias à ocorrência de incêndios, expressa sob a forma de índices de risco reveste-se de crucial importância para as operações de prevenção e combate aos incêndios, para além da mais-valia que constituem para os restantes sectores da sociedade. Adicionalmente, a detecção e monitorização de ocorrências de incêndios a partir de satélites tem vindo a assumir um papel preponderante, revelando-se fundamental enquanto fonte de alerta e pelas potencialidades que as bases de dados resultantes da observação contínua da Terra pelos satélites oferecem, no que às mais diversas áreas de investigação diz respeito. O presente trabalho explora, nas vertentes da prevenção de incêndios, a utilização de previsões de tempo de alta resolução espacial obtidas com o modelo WRF para a determinação das componentes do sistema FWI canadiano e sua disponibilização operacional. Os resultados apontam para um impacto positivo, em virtude do incremento da resolução espacial, da representação espacial do risco meteorológico de incêndio no território português. Paralelamente, apresentam-se os resultados do desenvolvimento de produtos de detecção de incêndios activos a partir da infra-estrutura de recepção de dados de satélite implementadas na Universidade de Aveiro, destacando a sua adequabilidade para a monitorização e identificação, em tempo quase-real, da ocorrência de fogos florestais em Portugal Continental.

Sistemas de automação e manutenção de edifícios: concepção dos sistemas de detecção e protecção contra incêndios de uma unidade industrial

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

Terrestrial ecosystems from space: a review of earth observation products for macroecology applications

Aim Earth observation (EO) products are a valuable alternative to spectral vegetation indices. We discuss the availability of EO products for analysing patterns in macroecology, particularly related to vegetation, on a range of spatial and temporal scales. Location Global. Methods We discuss four groups of EO products: land cover/cover change, vegetation structure and ecosystem productivity, fire detection, and digital elevation models. We address important practical issues arising from their use, such as assumptions underlying product generation, product accuracy and product transferability between spatial scales. We investigate the potential of EO products for analysing terrestrial ecosystems. Results Land cover, productivity and fire products are generated from long-term data using standardized algorithms to improve reliability in detecting change of land surfaces. Their global coverage renders them useful for macroecology. Their spatial resolution (e.g. GLOBCOVER vegetation, 300 m; MODIS vegetation and fire, ≥ 500 m; ASTER digital elevation, 30 m) can be a limiting factor. Canopy structure and productivity products are based on physical approaches and thus are independent of biome-specific calibrations. Active fire locations are provided in near-real time, while burnt area products show actual area burnt by fire. EO products can be assimilated into ecosystem models, and their validation information can be employed to calculate uncertainties during subsequent modelling. Main conclusions Owing to their global coverage and long-term continuity, EO end products can significantly advance the field of macroecology. EO products allow analyses of spatial biodiversity, seasonal dynamics of biomass and productivity, and consequences of disturbances on regional to global scales. Remaining drawbacks include inter-operability between products from different sensors and accuracy issues due to differences between assumptions and models underlying the generation of different EO products. Our review explains the nature of EO products and how they relate to particular ecological variables across scales to encourage their wider use in ecological applications.