A ground system for early forest fire detection based on infrared signal processing

This article presents a ground remote automatic system for forest surveillance based on infrared signal processing applied to early fire detection. Advanced techniques, which are based on infrared signal processing, are used in order to process the captured images. With the aim of determining the presence or absence of fire, the system performs the fusion of different detectors that exploit different expected characteristics of a real fire, such as persistence and increase. Theoretical simulations and practical results are presented to corroborate the control of the probability of false alarm. Results in a real environment are also presented to authenticate the accuracy of the operation of the proposed system. In particular, some experiments have been done to evaluate the delay of the system (tens of seconds on average) in detecting a controlled ground fire in a range of 1-10 km. Moreover, temporary evolution of false alarms and true detections are presented to evaluate the long-term performance of the system in a real environment. We have reached a detection probability of 100% at a false alarm rate of around 1 x 10(-9).This work has been supported by Generalitat Valenciana, under grant GVEMP06/001, and by MEC under the FPU programme.Bosch Roig, I.; Gómez, S.; Vergara Domínguez, L. (2011). A ground system for early forest fire detection based on infrared signal processing. International Journal of Remote Sensing. 32(17):4857-4870. https://doi.org/10.1080/01431161.2010.490245S485748703217Arrue, B. C., Ollero, A., & Matinez de Dios, J. R. (2000). An intelligent system for false alarm reduction in infrared forest-fire detection. IEEE Intelligent Systems, 15(3), 64-73. doi:10.1109/5254.846287Bernabeu, P., Vergara, L., Bosh, I., & Igual, J. (2004). A prediction/detection scheme for automatic forest fire surveillance. Digital Signal Processing, 14(5), 481-507. doi:10.1016/j.dsp.2004.06.003Briz, S. (2003). Reduction of false alarm rate in automatic forest fire infrared surveillance systems. Remote Sensing of Environment, 86(1), 19-29. doi:10.1016/s0034-4257(03)00064-6Pastor, E. (2003). Mathematical models and calculation systems for the study of wildland fire behaviour. Progress in Energy and Combustion Science, 29(2), 139-153. doi:10.1016/s0360-1285(03)00017-0Vergara, L., & Bernabeu, P. (2000). Automatic signal detection applied to fire control by infrared digital signal processing. Signal Processing, 80(4), 659-669. doi:10.1016/s0165-1684(99)00159-0Vergara, L., & Bernabeu, P. (2001). Simple approach to nonlinear prediction. Electronics Letters, 37(14), 926. doi:10.1049/el:20010616Vicente, J., & Guillemant, P. (2002). An image processing technique for automatically detecting forest fire. International Journal of Thermal Sciences, 41(12), 1113-1120. doi:10.1016/s1290-0729(02)01397-

Multisensor network system for wildfire detection using infrared image processing

This paper presents the next step in the evolution of multi-sensor wireless network systems in the early automatic detection of forest fires.This network allows remote monitoring of each of the locations as well as communication between each of the sensors and with the control stations.The result is an increased coverage area, with quicker and safer responses. To determine the presence of a forest wildfire, the system employs decision fusion in thermal imaging, which can exploit various expected characteristics of a real fire, including short-term persistence and long-term increases over time. Results from testing in the laboratory and in a real environment are presented to authenticate and verify the accuracy of the operation of the proposed system.The systemperformance is gauged by the number of alarms and the time to the first alarm (corresponding to a real fire), for different probability of false alarm (PFA).The necessity of including decision fusion is thereby demonstrated.This work has been supported by Generalitat Valenciana under Grant PROMETEO 2010-040 and Spanish Administration and European Union FEDER Programme under Grant TEC2011-23403 01/01/2012.Bosch Roig, I.; Serrano Cartagena, A.; Vergara Domínguez, L. (2013). Multisensor network system for wildfire detection using infrared image processing. The Scientific World Journal. https://doi.org/10.1155/2013/402196SRauste, Y., Herland, E., Frelander, H., Soini, K., Kuoremaki, T., & Ruokari, A. (1997). Satellite-based forest fire detection for fire control in boreal forests. International Journal of Remote Sensing, 18(12), 2641-2656. doi:10.1080/014311697217512Giglio, L., Descloitres, J., Justice, C. O., & Kaufman, Y. J. (2003). An Enhanced Contextual Fire Detection Algorithm for MODIS. Remote Sensing of Environment, 87(2-3), 273-282. doi:10.1016/s0034-4257(03)00184-6Carlotto, M. J. (1997). Detection and analysis of change in remotely sensed imagery with application to wide area surveillance. IEEE Transactions on Image Processing, 6(1), 189-202. doi:10.1109/83.552106Arrue, B. C., Ollero, A., & Matinez de Dios, J. R. (2000). An intelligent system for false alarm reduction in infrared forest-fire detection. IEEE Intelligent Systems, 15(3), 64-73. doi:10.1109/5254.846287Vicente, J., & Guillemant, P. (2002). An image processing technique for automatically detecting forest fire. International Journal of Thermal Sciences, 41(12), 1113-1120. doi:10.1016/s1290-0729(02)01397-2Briz, S. (2003). Reduction of false alarm rate in automatic forest fire infrared surveillance systems. Remote Sensing of Environment, 86(1), 19-29. doi:10.1016/s0034-4257(03)00064-6Martinez-de Dios, J. R., Arrue, B. C., Ollero, A., Merino, L., & Gómez-Rodríguez, F. (2008). Computer vision techniques for forest fire perception. Image and Vision Computing, 26(4), 550-562. doi:10.1016/j.imavis.2007.07.002Töreyin, B. U. (2007). Fire detection in infrared video using wavelet analysis. Optical Engineering, 46(6), 067204. doi:10.1117/1.2748752Lloret, J., Garcia, M., Bri, D., & Sendra, S. (2009). A Wireless Sensor Network Deployment for Rural and Forest Fire Detection and Verification. Sensors, 9(11), 8722-8747. doi:10.3390/s91108722Lloret, J., Bosch, I., Sendra, S., & Serrano, A. (2011). A Wireless Sensor Network for Vineyard Monitoring That Uses Image Processing. Sensors, 11(6), 6165-6196. doi:10.3390/s110606165Ho, C.-C. (2009). Machine vision-based real-time early flame and smoke detection. Measurement Science and Technology, 20(4), 045502. doi:10.1088/0957-0233/20/4/045502Günay, O., Taşdemir, K., Uğur Töreyin, B., & Enis Çetin, A. (2009). Video based wildfire detection at night. Fire Safety Journal, 44(6), 860-868. doi:10.1016/j.firesaf.2009.04.003Pastor, E. (2003). Mathematical models and calculation systems for the study of wildland fire behaviour. Progress in Energy and Combustion Science, 29(2), 139-153. doi:10.1016/s0360-1285(03)00017-0Vergara, L., & Bernabeu, P. (2000). Automatic signal detection applied to fire control by infrared digital signal processing. Signal Processing, 80(4), 659-669. doi:10.1016/s0165-1684(99)00159-0Vergara, L., & Bernabeu, P. (2001). Simple approach to nonlinear prediction. Electronics Letters, 37(14), 926. doi:10.1049/el:20010616Bernabeu, P., Vergara, L., Bosh, I., & Igual, J. (2004). A prediction/detection scheme for automatic forest fire surveillance. Digital Signal Processing, 14(5), 481-507. doi:10.1016/j.dsp.2004.06.003Bosch, I., Gómez, S., & Vergara, L. (2011). A ground system for early forest fire detection based on infrared signal processing. International Journal of Remote Sensing, 32(17), 4857-4870. doi:10.1080/01431161.2010.49024

Early forest fire detection by vision-enabled wireless sensor networks

Wireless sensor networks constitute a powerful technology particularly suitable for environmental monitoring. With regard to wildfires, they enable low-cost fine-grained surveillance of hazardous locations like wildland-urban interfaces. This paper presents work developed during the last 4 years targeting a vision-enabled wireless sensor network node for the reliable, early on-site detection of forest fires. The tasks carried out ranged from devising a robust vision algorithm for smoke detection to the design and physical implementation of a power-efficient smart imager tailored to the characteristics of such an algorithm. By integrating this smart imager with a commercial wireless platform, we endowed the resulting system with vision capabilities and radio communication. Numerous tests were arranged in different natural scenarios in order to progressively tune all the parameters involved in the autonomous operation of this prototype node. The last test carried out, involving the prescribed burning of a 95 x 20-m shrub plot, confirmed the high degree of reliability of our approach in terms of both successful early detection and a very low false-alarm rate. Journal compilationMinisterio de Ciencia e Innovación TEC2009-11812, IPT-2011-1625-430000Office of Naval Research (USA) N000141110312Centro para el Desarrollo Tecnológico e Industrial IPC-2011100

Unmanned Aerial Systems for Wildland and Forest Fires

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001

On the Analysis and Detection of Flames Withan Asynchronous Spiking Image Sensor

We have investigated the capabilities of a customasynchronous spiking image sensor operating in the NearInfrared band to study flame radiation emissions, monitortheir transient activity, and detect their presence. Asynchronoussensors have inherent capabilities, i.e., good temporal resolution,high dynamic range, and low data redundancy. This makesthem competitive against infrared (IR) cameras and CMOSframe-based NIR imagers. In this paper, we analyze, discuss,and compare the experimental data measured with our sensoragainst results obtained with conventional devices. A set ofmeasurements have been taken to study the flame emissionlevels and their transient variations. Moreover, a flame detectionalgorithm, adapted to our sensor asynchronous outputs, has beendeveloped. Results show that asynchronous spiking sensors havean excellent potential for flame analysis and monitoring.Universidad de Cádiz PR2016-07Ministerio de Economía y Competitividad TEC2015-66878-C3-1-RJunta de Andalucía TIC 2012-2338Office of Naval Research (USA) N00014141035

Video fire detection - Review

Cataloged from PDF version of article.This is a review article describing the recent developments in Video based Fire Detection (VFD). Video surveillance cameras and computer vision methods are widely used in many security applications. It is also possible to use security cameras and special purpose infrared surveillance cameras for fire detection. This requires intelligent video processing techniques for detection and analysis of uncontrolled fire behavior. VFD may help reduce the detection time compared to the currently available sensors in both indoors and outdoors because cameras can monitor “volumes” and do not have transport delay that the traditional “point” sensors suffer from. It is possible to cover an area of 100 km2 using a single pan-tiltzoom camera placed on a hilltop for wildfire detection. Another benefit of the VFD systems is that they can provide crucial information about the size and growth of the fire, direction of smoke propagation. © 2013 Elsevier Inc. All rights reserve