Optimal Fire Detection Using Image Processing

Flame detection and classification of flames is challenging work in the field of image processing. Use of image processing instead of sensors helps detection of fire in more accurate and quick manner. Thus this paper introduces a optimal approach for detection of fire using image processing technology which enables fire detection at the earliest in order to reduce life and property loss. The proposed system uses color segmentation model for detecting fire with reference to HSV and YCbCr color models. Also, density of fire growth is detected by the frame difference technique

BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis

Emergency events involving fire are potentially harmful, demanding a fast and precise decision making. The use of crowdsourcing image and videos on crisis management systems can aid in these situations by providing more information than verbal/textual descriptions. Due to the usual high volume of data, automatic solutions need to discard non-relevant content without losing relevant information. There are several methods for fire detection on video using color-based models. However, they are not adequate for still image processing, because they can suffer on high false-positive results. These methods also suffer from parameters with little physical meaning, which makes fine tuning a difficult task. In this context, we propose a novel fire detection method for still images that uses classification based on color features combined with texture classification on superpixel regions. Our method uses a reduced number of parameters if compared to previous works, easing the process of fine tuning the method. Results show the effectiveness of our method of reducing false-positives while its precision remains compatible with the state-of-the-art methods.Comment: 8 pages, Proceedings of the 28th SIBGRAPI Conference on Graphics, Patterns and Images, IEEE Pres

A LITERATURE STUDY ON IMAGE PROCESSING FOR FOREST FIRE DETECTION

Forests can purify water, stabilize soil, cycle nutrients, moderate climate, and store carbon. They can create habitat for wildlife and nurture environments rich in biological diversity. They can also contribute billions of dollars to the country’s economic wealth. However, hundreds of millions of hectares of forests are unfortunately devastated by forest fire each year. Forest fire has been constantly threatening to ecological systems, infrastructure, and public safety. In the image processing based forest fire detection using YCbCr colour model, method adopts rule based colour model due to its less complexity and effectiveness. YCbCr colour space effectively separates luminance from chrominance compared to other colour spaces like RGB. The method not only separates fire flame pixels but also separates high temperature fire centre pixels by taking in to account of statistical parameters of fire image in YCbCr colour space like mean and standard deviation. This paper presents a literature study on Image processing for forest fire detection

Computer Vision Application for Early Stage Smoke Detection on Ships

Nowadays, ship’s engine room is fire protected by automatic fire fighting systems, usually controlled from a place located outside the engine room. In order to activate the water mist extinguishing system automatically, at least two different fire detectors have to be activated. One of these detectors is a flame detector that is not hampered by various air flows caused by ventilation or draft and is rapidly activated and the other is smoke detector which is hampered by these flows causing its activation to be delayed. As a consequence, the automatic water mist extinguishing system is also delayed, allowing for fire expansion and its transfer to surrounding rooms. In addition to reliability of the ship’s fire detection system as one of the crucial safety features for the ship, cargo, crew and passengers, using a systematic approach in this research the emphasis is placed on the application of new methods in smoke detection such as the computer image processing and analysis, in order to achieve this goal. This paper describes the research carried out on board ship using the existing marine CCTV systems in early stages of smoke detection inside ship’s engine room, which could be seen as a significant contribution to accelerated suppression of unwanted consequences

DETEKSI DINI KEBAKARAN HUTAN DAN LAHAN MEMANFAATKAN EKSTRAKSI EXIF PADA INFORMASI GAMBAR BERBASIS PENGOLAHAN CITRA

Forest fire detection system is one of important tools in preventing and mitigating forest and land fires. In Indonesia, the detection of forest and land fires relies on hotspot information captured from satellites. However, the location obtained by the satellite has a horizontal error of 2 km from the ground check data. Therefore, these information are less relevant to the actual location. In this research, an android app is proposed to extract Exchangeable Image Format (EXIF) photo metadata. The metadata has image information such as latitude and longitude, to obtain the location of forest fires reported by the application user. In addition, this research implemented one of the image processing methods to classify fire and smoke in images of fires. Color filtering method is used based on the color space of Red Green Blue (RGB), Hue Saturation Value (HSV) and YCbCr. This classification process aims to ease the burden on the admin in confirming user reports. The results of the fire and smoke classification process are described using a confusion matrix. This matrix  produces an accuracy rate of 75%, a precision of 80% and a recall of 80% for a fire classification and an accuracy of 70%, a precision of 92% and a recall of 87% for smoke classification. There are 25% and 30% of misclassified data of fire and smoke. This is because the color filtering method classifies each color pixel from the image, therefore many pixels that are not classified as fire or smoke images are classified because there are other objects that have a range of colors to classify fire and smok

DETEKSI SEBARAN TITIK API PADA KEBAKARAN HUTAN GAMBUT MENGGUNAKAN GELOMBANG-SINGKAT DAN BACKPROPAGATION (STUDI KASUS KOTA DUMAI PROVINSI RIAU)

Peat moss forest fire is one of damaging disasters. It disturbs people’s activity and health and also reduces ecological livings; thus, this disaster becomes attention of large society either nationally or internationally. Peat moss forest fire can be identified by remote sensing technology. The development of remote sensing technology by using MODIS imaging satellite so far has been used in many fields and one of them is in controlling forest fire multitemporally. Hotspots of information obtained from the MODIS image processing can be obtained by image feature extraction using a wavelet , so that the results of this extraction can be input in the process of detection of hotspots using Artificial Neural Networks . In this research data for surface temperature used Terra MODIS satellite by taking an advantage of canal 31 and 32 and using Coll’s, et.al (1994) algorithm, The data of forest fire were from forest fire service of Dumai City, for feature extraction using a wavelet Haar , Coiflet1 and Symlet5, and backpropagation neural networks to recognize patterns hotspot. This research was conducted in Dumai City Province of Riau. Result of this research shows that the use of Canal 31 and 32 through Terra MODIS imaging satellite can be used for detecting points of fire which is found that there are more then 17 points of fire within temperature ranges of 270C – 320C, While the results of the Pattern Recognition hotspots using short - wave and Backpropagation Network with image input in the form of satellite images with 8 bit and size 512 x 512 derived from satellite data turned out to give good results with a performance of 100 % on the image of the wavelet decomposition Haar and Coiflet 1 , while for wavelet Symlet5 gives performance by 40 %

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

Dynamic texture analysis in video with application to flame, smoke and volatile organic compound vapor detection

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Science of Bilkent University, 2009.Thesis (Master's) -- Bilkent University, 2009.Includes bibliographical references leaves 74-82.Dynamic textures are moving image sequences that exhibit stationary characteristics in time such as fire, smoke, volatile organic compound (VOC) plumes, waves, etc. Most surveillance applications already have motion detection and recognition capability, but dynamic texture detection algorithms are not integral part of these applications. In this thesis, image processing based algorithms for detection of specific dynamic textures are developed. Our methods can be developed in practical surveillance applications to detect VOC leaks, fire and smoke. The method developed for VOC emission detection in infrared videos uses a change detection algorithm to find the rising VOC plume. The rising characteristic of the plume is detected using a hidden Markov model (HMM). The dark regions that are formed on the leaking equipment are found using a background subtraction algorithm. Another method is developed based on an active learning algorithm that is used to detect wild fires at night and close range flames. The active learning algorithm is based on the Least-Mean-Square (LMS) method. Decisions from the sub-algorithms, each of which characterize a certain property of the texture to be detected, are combined using the LMS algorithm to reach a final decision. Another image processing method is developed to detect fire and smoke from moving camera video sequences. The global motion of the camera is compensated by finding an affine transformation between the frames using optical flow and RANSAC. Three frame change detection methods with motion compensation are used for fire detection with a moving camera. A background subtraction algorithm with global motion estimation is developed for smoke detection.Günay, OsmanM.S