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Relevant bibliographies by topics / Fire detection

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Academic literature on the topic 'Fire detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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Journal articles on the topic "Fire detection"

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Ramya Laxmi, K., A. Sreeja, E. Revanth, Manasa Gourishetty, and M. Rushikesh. "AUTOMATED FIRE DETECTION AND SURVEILLANCE SYSTEM." YMER Digital 21, no. 04 (April 24, 2022): 432–37. http://dx.doi.org/10.37896/ymer21.04/41.

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The major goal of this project is to build a fire detection and surveillance system that is automatic. During surveillance, Convolutional Neural Networks will be employed to detect the fire (CNNs). Such methods, on the other hand, typically need greater processing time and memory, limiting their use in surveillance networks. We propose a low-cost fire detection CNN architecture for surveillance films in this research. This is mostly concerned with computational complexity and detection precision. The model is fine-tuned to balance efficiency and accuracy, taking into account the nature of the target problem and fire data. This system takes an image or video file as input and detects fire and fire percentages that are precise enough to prevent fire mishaps and save human lives.

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Dandge, Shrikant. "Survey on Fire Detection Using Image Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 2048–57. http://dx.doi.org/10.22214/ijraset.2022.47749.

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Abstract: With rising Urbanisation the frequency of fires has increased. A rapid need exists for quick and effective fire detection. Traditional fire detection systems are utilizing physical sensors to detect fire. Sensors gather information about the chemical characteristics of airborne particles, which traditional fire detection systems then use to generate an alarm. However, it can also result in false alerts; for instance, an ordinary fire alarm system might be triggered by smoking inside a space. Using a computer system based on vision for detecting fire would facilitate rapid and precise detection of fire with the ongoing developments in image processing. A lot of observable improvements have been developed to help a successful fire detection algorithm or model. This paper compiles research on methods that, when used, can effectively detect fire. In addition, a system architecture for fire detection is developed in this study. It suggests many fire detection methods, including Celik, SDD, F-RCNN, R-FCN and YOLOv3. This paper offers a thorough comparison of the same.

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Le Maoult, Y., T. Sentenac, J. J. Orteu, and J. P. Arcens. "Fire Detection." Process Safety and Environmental Protection 85, no. 3 (January 2007): 193–206. http://dx.doi.org/10.1205/psep06035.

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Lu, Xiaoman, Xiaoyang Zhang, Fangjun Li, Mark A. Cochrane, and Pubu Ciren. "Detection of Fire Smoke Plumes Based on Aerosol Scattering Using VIIRS Data over Global Fire-Prone Regions." Remote Sensing 13, no. 2 (January 8, 2021): 196. http://dx.doi.org/10.3390/rs13020196.

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Smoke from fires significantly influences climate, weather, and human health. Fire smoke is traditionally detected using an aerosol index calculated from spectral contrast changes. However, such methods usually miss thin smoke plumes. It also remains challenging to accurately separate smoke plumes from dust, clouds, and bright surfaces. To improve smoke plume detections, this paper presents a new scattering-based smoke detection algorithm (SSDA) depending mainly on visible and infrared imaging radiometer suite (VIIRS) blue and green bands. The SSDA is established based on the theory of Mie scattering that occurs when the diameter of an atmospheric particulate is similar to the wavelength of the scattered light. Thus, smoke commonly causes Mie scattering in VIIRS blue and green bands because of the close correspondence between smoke particulate diameters and the blue/green band wavelengths. For developing the SSDA, training samples were selected from global fire-prone regions in North America, South America, Africa, Indonesia, Siberia, and Australia. The SSDA performance was evaluated against the VIIRS aerosol detection product and smoke detections from the ultraviolet aerosol index using manually labeled fire smoke plumes as a benchmark. Results show that the SSDA smoke detections are superior to existing products due chiefly to the improved ability of the algorithm to detect thin smoke and separate fire smoke from other surface types. Moreover, the SSDA smoke distribution pattern exhibits a high spatial correlation with the global fire density map, suggesting that SSDA is capable of detecting smoke plumes of fires in near real-time across the globe.

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Lu, Xiaoman, Xiaoyang Zhang, Fangjun Li, Mark A. Cochrane, and Pubu Ciren. "Detection of Fire Smoke Plumes Based on Aerosol Scattering Using VIIRS Data over Global Fire-Prone Regions." Remote Sensing 13, no. 2 (January 8, 2021): 196. http://dx.doi.org/10.3390/rs13020196.

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Smoke from fires significantly influences climate, weather, and human health. Fire smoke is traditionally detected using an aerosol index calculated from spectral contrast changes. However, such methods usually miss thin smoke plumes. It also remains challenging to accurately separate smoke plumes from dust, clouds, and bright surfaces. To improve smoke plume detections, this paper presents a new scattering-based smoke detection algorithm (SSDA) depending mainly on visible and infrared imaging radiometer suite (VIIRS) blue and green bands. The SSDA is established based on the theory of Mie scattering that occurs when the diameter of an atmospheric particulate is similar to the wavelength of the scattered light. Thus, smoke commonly causes Mie scattering in VIIRS blue and green bands because of the close correspondence between smoke particulate diameters and the blue/green band wavelengths. For developing the SSDA, training samples were selected from global fire-prone regions in North America, South America, Africa, Indonesia, Siberia, and Australia. The SSDA performance was evaluated against the VIIRS aerosol detection product and smoke detections from the ultraviolet aerosol index using manually labeled fire smoke plumes as a benchmark. Results show that the SSDA smoke detections are superior to existing products due chiefly to the improved ability of the algorithm to detect thin smoke and separate fire smoke from other surface types. Moreover, the SSDA smoke distribution pattern exhibits a high spatial correlation with the global fire density map, suggesting that SSDA is capable of detecting smoke plumes of fires in near real-time across the globe.

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Lin, Ji, Haifeng Lin, and Fang Wang. "A Semi-Supervised Method for Real-Time Forest Fire Detection Algorithm Based on Adaptively Spatial Feature Fusion." Forests 14, no. 2 (February 11, 2023): 361. http://dx.doi.org/10.3390/f14020361.

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Forest fires occur frequently around the world, causing serious economic losses and human casualties. Deep learning techniques based on convolutional neural networks (CNN) are widely used in the intelligent detection of forest fires. However, CNN-based forest fire target detection models lack global modeling capabilities and cannot fully extract global and contextual information about forest fire targets. CNNs also pay insufficient attention to forest fires and are vulnerable to the interference of invalid features similar to forest fires, resulting in low accuracy of fire detection. In addition, CNN-based forest fire target detection models require a large number of labeled datasets. Manual annotation is often used to annotate the huge amount of forest fire datasets; however, this takes a lot of time. To address these problems, this paper proposes a forest fire detection model, TCA-YOLO, with YOLOv5 as the basic framework. Firstly, we combine the Transformer encoder with its powerful global modeling capability and self-attention mechanism with CNN as a feature extraction network to enhance the extraction of global information on forest fire targets. Secondly, in order to enhance the model’s focus on forest fire targets, we integrate the Coordinate Attention (CA) mechanism. CA not only acquires inter-channel information but also considers direction-related location information, which helps the model to better locate and identify forest fire targets. Integrated adaptively spatial feature fusion (ASFF) technology allows the model to automatically filter out useless information from other layers and efficiently fuse features to suppress the interference of complex backgrounds in the forest area for detection. Finally, semi-supervised learning is used to save a large amount of manual labeling effort. The experimental results show that the average accuracy of TCA-YOLO improves by 5.3 compared with the unimproved YOLOv5. TCA-YOLO also outperformed in detecting forest fire targets in different scenarios. The ability of TCA-YOLO to extract global information on forest fire targets was much improved. Additionally, it could locate forest fire targets more accurately. TCA-YOLO misses fewer forest fire targets and is less likely to be interfered with by forest fire-like targets. TCA-YOLO is also more focused on forest fire targets and better at small-target forest fire detection. FPS reaches 53.7, which means that the detection speed meets the requirements of real-time forest fire detection.

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Johnston, Joshua, Lynn Johnston, Martin Wooster, Alison Brookes, Colin McFayden, and Alan Cantin. "Satellite Detection Limitations of Sub-Canopy Smouldering Wildfires in the North American Boreal Forest." Fire 1, no. 2 (August 10, 2018): 28. http://dx.doi.org/10.3390/fire1020028.

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We develop a simulation model for prediction of forest canopy interception of upwelling fire radiated energy from sub-canopy smouldering vegetation fires. We apply this model spatially across the North American boreal forest in order to map minimum detectable sub-canopy smouldering fire size for three satellite fire detection systems (sensor and algorithm), broadly representative of the Moderate Resolution Imaging Spectroradiometer (MODIS), Sea and Land Surface Temperature Radiometer (SLSTR) and Visible Infrared Imaging Radiometer Suite (VIIRS). We evaluate our results according to fire management requirements for “early detection” of wildland fires. In comparison to the historic fire archive (Canadian National Fire Database, 1980–2017), satellite data with a 1000 m pixel size used with an algorithm having a minimum MWIR channel BT elevation threshold of 5 and 3 K above background (e.g., MODIS or SLSTR) proves incapable of providing a sub-0.2 ha smouldering fire detection 70% and 45% of the time respectively, even assuming that the sensor overpassed the relevant location within the correct time window. By contrast, reducing the pixel area by an order of magnitude (e.g., 375 m pixels of VIIRS) and using a 3.5 K active fire detection threshold offers the potential for successfully detecting all fires when they are still below 0.2 ha. Our results represent a ‘theoretical best performance’ of remote sensing systems to detect sub-canopy smoldering fires early in their lifetime.

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Ryu, Jinkyu, and Dongkurl Kwak. "Flame Detection Using Appearance-Based Pre-Processing and Convolutional Neural Network." Applied Sciences 11, no. 11 (May 31, 2021): 5138. http://dx.doi.org/10.3390/app11115138.

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It is important for fire detectors to operate quickly in the event of a fire, but existing conventional fire detectors sometimes do not work properly or there are problems where non-fire or false reporting occurs frequently. Therefore, in this study, HSV color conversion and Harris Corner Detection were used in the image pre-processing step to reduce the incidence of false detections. In addition, among the detected corners, the vicinity of the corner point facing the upper direction was extracted as a region of interest (ROI), and the fire was determined using a convolutional neural network (CNN). These methods were designed to detect the appearance of flames based on top-pointing properties, which resulted in higher accuracy and higher precision than when input images were still used in conventional object detection algorithms. This also reduced the false detection rate for non-fires, enabling high-precision fire detection.

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Meidelfi, Dwiny, Hanriyawan Adnan Moodutor, Fanni Sukma, and Sandri Adnin. "Android Based Spark and Gas Leak Detection and Monitoring." Journal of Computer Networks, Architecture and High Performance Computing 4, no. 2 (July 21, 2022): 148–57. http://dx.doi.org/10.47709/cnahpc.v4i2.1489.

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LPG cylinder leakage is one of the causes of fires in the community. To prevent fires, a fire and gas leak detection and monitoring device were made using a fire detector sensor and an Android-based MQ-6 to trigger it. Data collection techniques in the manufacture of gas and fire leak detection using a flame detector and the MQ-6 sensor can be obtained from datasheets, journals, books and articles, and several internet sites that support the manufacture of this device. In the manufacture of gas leak detection devices or tools, there are also two parts, namely the first to make hardware (hardware), then software (software). The result of this tool detection is that users can find out the level of LPG due to leaking of LPG cylinders and detect fire using Android notifications in real-time and the data is displayed in detail on the browser page. The conclusion of this study is that users are safer because there is a gas leak, the tool will detect LPG gas, then a message will be displayed on the LCD screen and a notification on Android and the buzzer will automatically turn on. If there is a fire from detecting the gas leak, the fire detector will detect the fire, which will result in a notification sent to Android that there is a fire and the buzzer will turn on

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Sun, Lijun. "Digital Print Synthesis Based on Image Processing and Interactive Technology." Journal of Physics: Conference Series 2146, no. 1 (January 1, 2022): 012028. http://dx.doi.org/10.1088/1742-6596/2146/1/012028.

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Abstract Fire is a common disaster, which causes major threats and losses to human life and property. Countries around the world have been committed to the study of the mechanism and internal mechanism of fires, with the goal of preventing fires from occurring and minimizing the losses caused by fires. Among the many methods, fire detection technology is an effective method to prevent and reduce the occurrence of fire. This article focuses on the research of the fire detection system based on artificial intelligence technology, improves the accuracy of the fire detection system by introducing artificial intelligence technology into the fire detection system, and uses experiments to verify the error rate of the artificial intelligence technology fire detection system. The experimental results show that the system’s detection of fire is not very different from the actual situation, and the error rate is within 10%. Then compared with the traditional detection system, the detection performance is relatively high, and the error rate can be reduced by one time.

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Dissertations / Theses on the topic "Fire detection"

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Al-Khateeb, Shadi A. "Fire Detection Using Wireless Sensor Networks." Youngstown State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1411471850.

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Luisi, Domenico. "Conceptual design and specification of a microsatellite forest fire detection system /." Online version of thesis, 2007. http://hdl.handle.net/1850/5771.

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Zafar, Muhammad Asif, and Zeshan Aslam Khan. "Fire Detection in Coal Mines Using WSN." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-5030.

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Fire Detection in Coal Mines Using WSN is an application for monitoring and detection of fire in coal mines using wireless sensor networks. The application uses BDI (Belief, Desire and Intention) based multi agent model and its implementation on sensor networks. The Language which is interpreted by Jason is an extension of AgentSpeak; this is based on the BDI Architecture. The BDI agents are reactive planning systems, systems that are not meant to compute the value of a function and terminate, but rather designed to be permanently running, reacting to some form of event. The distributed model of environment is adopted to overcome the communication overhead, power consumption, network delay and reliability on a centralized base station.

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Krasuski, Adam, and Piotr Wasilewski. "The Detection of Outlying Fire Service’s Reports." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-113145.

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We present a methodology for improving the detection of outlying Fire Service’s reports based on domain knowledge and dialogue with Fire & Rescue domain experts. The outlying report is considered as element which is significantly different from the remaining data. Outliers are defined and searched on the basis of domain knowledge and dialogue with experts. We face the problem of reducing high data dimensionality without loosing specificity and real complexity of reported incidents. We solve this problem by introducing a knowledge based generalization level intermediating between analysed data and experts domain knowledge. In the methodology we use the Formal Concept Analysis methods for both generation appropriate categories from data and as tools supporting communication with domain experts. We conducted two experiments in finding two types of outliers in which outliers detection was supported by domain experts.

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True, Nicholas James. "Real-time fire detection in low quality video." Diss., [La Jolla] : University of California, San Diego, 2010. http://wwwlib.umi.com/cr/fullcit?p1477940.

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Thesis (M.S.)--University of California, San Diego, 2010.
Title from first page of PDF file (viewed July 13, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (leaves 63-65).

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Rexfort, Claudia. "A contribution to fire detection modelling and simulation." [S.l. : s.n.], 2004. http://deposit.ddb.de/cgi-bin/dokserv?idn=971472572.

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Kutzner, Kendy. "Processing MODIS Data for Fire Detection in Australia." Thesis, Universitätsbibliothek Chemnitz, 2002. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200200831.

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The aim of this work was to use remote sensing data from the MODIS instrument of the Terra satellite to detect bush fires in Australia. This included preprocessing the demodulator output, bit synchronization and reassembly of data packets. IMAPP was used to do the geolocation and data calibration. The fire detection used a combination of fixed threshold techniques with difference tests and background comparisons. The results were projected in a rectangular latidue/longitude map to remedy the bow tie effect. Algorithms were implemented in C and Matlab. It proved to be possible to detect fires in the available data. The results were compared with fire detection done done by NASA and fire detections based on other sensors and found to be very similar
Das Ziel dieser Arbeit war die Nutzung von Fernerkundungsdaten des MODIS Instruments an Bord des Satelliten Terra zur Erkennung von Buschfeuern in Australien. Das schloss die Vorverarbeitung der Daten vom Demodulator, die Bitsynchronisation und die Umpacketierung der Daten ein. IMAPP wurde genutzt um die Daten zu kalibrieren und zu geolokalisieren. Die Feuererkennung bedient sich einer Kombination von absoluten Schwellwerttests, Differenztests und Vergleichen mit dem Hintergrund. Die Ergebnisse wurden in eine rechteckige Laengen/Breitengradkarte projiziert um dem BowTie Effekt entgegenzuwirken. Die benutzten Algrorithmen wurden in C und Matlab implementiert. Es zeigte sich, dass es moeglich ist in den verfuegbaren Daten Feuer zu erkennen. Die Ergebnisse wurden mit Feuererkennungen der NASA und Feuererkennung die auf anderen Sensoren basieren verglichen und fuer sehr aehnlich befunden

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Rehman, Adeel ur. "Autonomous Fire Detection Robot Using Modified Voting Logic." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32008.

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Recent developments at Fukushima Nuclear Power Plant in Japan have created urgency in the scientist community to come up with solutions for hostile industrial environment in case of a breakdown or natural disaster. There are many hazardous scenarios in an indoor industrial environment such as risk of fire, failure of high speed rotary machines, chemical leaks, etc. Fire is one of the leading causes for workplace injuries and fatalities. The current fire protection systems available in the market mainly consist of a sprinkler systems and personnel on duty. In the case of a sprinkler system there could be several things that could go wrong, such as spraying water on a fire created by an oil leak may even spread it, the water from the sprinkler system may harm the machinery in use that is not under the fire threat and the water could potentially destroy expensive raw material, finished goods and valuable electronic and printed data. There is a dire need of an inexpensive autonomous system that can detect and approach the source of these hazardous scenarios. This thesis focuses mainly on industrial fires but, using same or similar techniques on different sensors, may allow it to detect and approach other hostile situations in industrial workplace. Autonomous robots can be equipped to detect potential threats of fire and find out the source while avoiding the obstacles during navigation. The proposed system uses Modified Voting Logic Fusion to approach and declare a potential fire source autonomously. The robot follows the increasing gradient of light and heat intensity to identify the threat and approach the source.

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Osgood, David Raymond. "The detection of the early stages of fire." Thesis, London South Bank University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336804.

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Lawday, R. M. "The automatic detection of audible fire alarm warnings." Thesis, Birmingham City University, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.573693.

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Books on the topic "Fire detection"

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Bennett, Roger P. Fire detection. New York: Nova Science Publishers, 2011.

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Bennett, Roger P., and Roger P. Bennett. Fire detection. New York: Nova Science Publishers, 2011.

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Lynne, Murnane, Ruane Thomas P, and International Fire Service Training Association., eds. Fire detection and suppression systems. 3rd ed. Stillwater, OK: Fire Protection Publications, Oklahoma State University, 2005.

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A, Wieder Michael, Smith Carol M, and Brakhage Cynthia, eds. Private fire protection and detection. 2nd ed. Stillwater, Okla: Fire Protection Publications, Oklahoma State University, 1994.

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Great Britain. Department of Health. Firecode: Alarm and detection systems. London: HMSO, 1989.

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L, Bryan John. Fire suppression and detection systems. 3rd ed. New York: Macmillan, 1993.

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Zalosh, Robert G. International road tunnel fire detection research project. Quincy, MA: Fire Protection Research Foundation, 2003.

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Riksantikvariatet, Norway, and Historic Scotland. Technical Conservation, Research and Education Group, eds. Minimum invasive fire detection for protection of heritage. Oslo: Riksantikvaren, 2006.

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Litton, C. D. Fire detection for conveyor belt entries. Washington, D.C: U.S. Dept. of the Interior, Bureau of Mines, 1991.

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Dinaburg, Joshua, and Daniel T. Gottuk. Fire Detection in Warehouse Facilities. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-8115-7.

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Book chapters on the topic "Fire detection"

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Till, Robert C., and J. Walter Coon. "Other Detection and Alarm Devices." In Fire Protection, 27–38. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90844-1_3.

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Nowzad, Azarm. "Ground-Based Fire Detection." In Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, 1–6. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-51727-8_143-1.

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Nowzad, Azarm. "Airborne-Based Fire Detection." In Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, 1–5. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-51727-8_146-1.

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Schiks, Tom, Alan S. Cantin, Joshua M. Johnston, Ronan Paugam, and Ellen Whitman. "Satellite-Based Fire Detection." In Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, 1–4. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-51727-8_148-1.

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Nowzad, Azarm. "Ground-Based Fire Detection." In Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, 570–76. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-52090-2_143.

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Nowzad, Azarm. "Airborne-Based Fire Detection." In Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, 10–16. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-52090-2_146.

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Schiks, Tom, Alan S. Cantin, Joshua M. Johnston, Ronan Paugam, and Ellen Whitman. "Satellite-Based Fire Detection." In Encyclopedia of Wildfires and Wildland-Urban Interface (WUI) Fires, 897–900. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-52090-2_148.

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Dinaburg, Joshua, and Daniel T. Gottuk. "Fire Detection in Warehouse Facilities." In Fire Detection in Warehouse Facilities, 1–59. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-8115-7_1.

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Ingason, Haukur, Ying Zhen Li, and Anders Lönnermark. "Fire Suppression and Detection in Tunnels." In Tunnel Fire Dynamics, 403–43. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-2199-7_16.

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Pundir, Arun Singh, Himanshu Buckchash, Amitesh Singh Rajput, Vishesh Kumar Tanwar, and Balasubramanian Raman. "Fire Detection Using Dense Trajectories." In Proceedings of 2nd International Conference on Computer Vision & Image Processing, 211–21. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7898-9_17.

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Conference papers on the topic "Fire detection"

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Mikhanoshina, Julia L., and Eugene V. Sypin. "On the choice of fire detection principles by combined fire detector." In 2015 16th International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices (EDM). IEEE, 2015. http://dx.doi.org/10.1109/edm.2015.7184550.

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Hwang, Jung-Hoon, Sewoong Jun, Seung-Hun Kim, Donghoon Cha, Kaehoon Jeon, and Jongbae Lee. "Novel fire detection device for robotic fire fighting." In 2010 International Conference on Control, Automation and Systems (ICCAS 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccas.2010.5669964.

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Silva, Lucas Tejedor da, João T. Dias, and Myrna Cunha. "Intelligent fire detection device." In XXXIX Simpósio Brasileiro de Telecomunicações e Processamento de Sinais. Sociedade Brasileira de Telecomunicações, 2021. http://dx.doi.org/10.14209/sbrt.2021.1570722958.

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Silva, Lucas Tejedor da, João T. Dias, and Myrna Cunha. "Intelligent fire detection device." In XXXIX Simpósio Brasileiro de Telecomunicações e Processamento de Sinais. Sociedade Brasileira de Telecomunicações, 2021. http://dx.doi.org/10.14209/sbrt.2021.1570722958.

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Divan, Afroj, Akshay Sharath Kumar, Aswin J. Kumar, Anjana Jain, and S. Ravishankar. "FIRE DETECTION USING QUADCOPTER." In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2018. http://dx.doi.org/10.1109/iccons.2018.8663134.

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Che-Bin Liu and N. Ahuja. "Vision based fire detection." In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1333722.

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Shuhai, Wang, Chen Shuxin, Chen shuwang, and An shengbiao. "Experimental Research on Fire Smoke for Fire Automatic Detection." In 2007 8th International Conference on Electronic Measurement and Instruments. IEEE, 2007. http://dx.doi.org/10.1109/icemi.2007.4351122.

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Zhong, Shuxin, Yongzhi Huang, Rukhsana Ruby, Lu Wang, Yu-Xuan Qiu, and Kaishun Wu. "Wi-fire: Device-free fire detection using WiFi networks." In ICC 2017 - 2017 IEEE International Conference on Communications. IEEE, 2017. http://dx.doi.org/10.1109/icc.2017.7997406.

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Ramasubramanian, Sreesruthi, Senthil Arumugam Muthukumaraswamy, and A. Sasikala. "Fire Detection using Artificial Intelligence for Fire-Fighting Robots." In 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2020. http://dx.doi.org/10.1109/iciccs48265.2020.9121017.

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Hillman, Thomas C., and William R. Kane. "Aircraft Fire Detection and Suppression." In Aerospace Technology Conference and Exposition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1990. http://dx.doi.org/10.4271/901951.

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Reports on the topic "Fire detection"

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Jason, Nora H. Spacecraft fire detection and extinguishment :. Gaithersburg, MD: National Bureau of Standards, 1988. http://dx.doi.org/10.6028/nbs.ir.88-3712.

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Bukowski, Richard W., and Nora H. Jason. International fire detection bibliography 1975 - 1990. Gaithersburg, MD: National Institute of Standards and Technology, 1991. http://dx.doi.org/10.6028/nist.ir.4661.

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Stroup, David W., David D. Evans, and Phyllis Martin. Evaluating thermal fire detection systems (English units). Gaithersburg, MD: National Bureau of Standards, 1986. http://dx.doi.org/10.6028/nbs.sp.712.

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Stroup, David W., David D. Evans, and Phyllis Martin. Evaluating thermal fire detection systems (SI units). Gaithersburg, MD: National Bureau of Standards, 1986. http://dx.doi.org/10.6028/nbs.sp.713.

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Beall, Kellie A., Kellie A. Beall, William Lytle Grosshandler, and Heinz Luck. 12th International Conference on Automatic Fire Detection. Gaithersburg, MD: National Institute of Standards and Technology, 2001. http://dx.doi.org/10.6028/nist.sp.965.

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Chakravorty, R. N. Early detection of fire in coal storage facilities. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1985. http://dx.doi.org/10.4095/304793.

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Wong, Jennifer T., Daniel T. Gottuk, Susan L. Rose-Pehrsson, Ronald B. Shaffer, and Sean Hart. Results of Multi-Criteria Fire Detection System Tests. Fort Belvoir, VA: Defense Technical Information Center, May 2000. http://dx.doi.org/10.21236/ada378034.

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Gottuk, Daniel T., Matthew Harrison, Joseph L. Scheffey, Susan L. Rose-Pehrsson, Frederick W. Williams, and John P. Farley. An Initial Evaluation of Video-Based Fire Detection Technologies. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada440353.

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Wright, Mark T., Daniel T. Gottuk, Jennifer T. Wong, Susan L. Rose-Pehrsson, and Sean Hart. Prototype Early Warning Fire Detection System: Test Series 1 Results. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada382542.

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Wright, Mark T., Daniel T. Gottuk, Jennifer T. Wong, Hung Pham, and Susan L. Rose-Pehrsson. Prototype Early Warning Fire Detection System: Test Series 2 Results. Fort Belvoir, VA: Defense Technical Information Center, October 2000. http://dx.doi.org/10.21236/ada383972.

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