EARLY FOREST FIRE DETECTION USING TEXTURE, BLOB THRESHOLD, AND MOTION ANALYSIS OF PRINCIPAL COMPONENTS

Forest fires constantly threaten ecological systems, infrastructure and human lives. The purpose behind this study is minimizing the devastating damage caused by forest fires. Since it is impossible to completely avoid their occurrences, it is essential to accomplish a fast and appropriate intervention to minimize their destructive consequences. The most traditional method for detecting forest fires is human based surveillance through lookout towers. However, this study presents a more modern technique. It utilizes land-based real-time multispectral video processing to identify and determine the possibility of fire occurring within the camera’s field of view. The temporal, spectral, and spatial signatures of the fire are exploited. The methods discussed include: (1) Range filtering followed by entropy filtering of the infrared (IR) video data, and (2) Principal Component Analysis of visible spectrum video data followed by motion analysis and adaptive intensity threshold. The two schemes presented are tailored to detect the fire core, and the smoke plume, respectively. Cooled Midwave Infrared (IR) camera is used to capture the heat distribution within the field of view. The fire core is then isolated using texture analysis techniques: first, range filtering applied on two consecutive IR frames, and then followed by entropy filtering of their absolute difference. Since smoke represents the earliest sign of fire, this study also explores multiple techniques for detecting smoke plumes in a given scene. The spatial and temporal variance of smoke plume is captured using temporal Principal Component Analysis, PCA. The results show that a smoke plume is readily segmented via PCA applied on the visible Blue band over 2 seconds sampled every 0.2 seconds. The smoke plume exists in the 2nd principal component, and is finally identified, segmented, and isolated, using either motion analysis or adaptive intensity threshold. Experimental results, obtained in this study, show that the proposed system can detect smoke effectively at a distance of approximately 832 meters with a low false-alarm rate and short reaction time. Applied, such system would achieve early forest fire detection minimizing fire damage. Keywords: Image Processing, Principal Component Analysis, PCA, Principal Component, PC, Texture Analysis, Motion Analysis, Multispectral, Visible, Cooled Midwave Infrared, Smoke Signature, Gaussian Mixture Model

Video Based Flame Detection Using Spatio-Temporal Features and SVM Classification

Video-based surveillance systems can be used for early fire detection and localization in order to minimize the damage and casualties caused by wildfires. However, reliability of these systems is an important issue and therefore early detection versus false alarm rate has to be considered. In this paper, we present a new algorithm for video based flame detection, which identifies spatio-temporal features of fire such as colour probability, contour irregularity, spatial energy, flickering and spatio-temporal energy. For each candidate region of an image a feature vector is generated and used as input to an SVM classifier, which discriminates between fire and fire-coloured regions. Experimental results show that the proposed methodology provides high fire detection rates with a reasonable false alarm ratio

Flame Detection for Video-based Early Fire Warning Systems and 3D Visualization of Fire Propagation

Early and accurate detection and localization of flame is an essential requirement of modern early fire warning systems. Video-based systems can be used for this purpose; however, flame detection remains a challenging issue due to the fact that many natural objects have similar characteristics with fire. In this paper, we present a new algorithm for video based flame detection, which employs various spatio-temporal features such as colour probability, contour irregularity, spatial energy, flickering and spatio-temporal energy. Various background subtraction algorithms are tested and comparative results in terms of computational efficiency and accuracy are presented. Experimental results with two classification methods show that the proposed methodology provides high fire detection rates with a reasonable false alarm ratio. Finally, a 3D visualization tool for the estimation of the fire propagation is outlined and simulation results are presented and discussed.The original article was published by ACTAPRESS and is available here: http://www.actapress.com/Content_of_Proceeding.aspx?proceedingid=73

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

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

Automatic Fire Detection: A Survey from Wireless Sensor Network Perspective

Automatic fire detection is important for early detection and promptly extinguishing fire. There are ample studies investigating the best sensor combinations and appropriate techniques for early fire detection. In the previous studies fire detection has either been considered as an application of a certain field (e.g., event detection for wireless sensor networks) or the main concern for which techniques have been specifically designed (e.g., fire detection using remote sensing techniques). These different approaches stem from different backgrounds of researchers dealing with fire, such as computer science, geography and earth observation, and fire safety. In this report we survey previous studies from three perspectives: (1) fire detection techniques for residential areas, (2) fire detection techniques for forests, and (3) contributions of sensor networks to early fire detection