Relevant bibliographies by topics / Smoke and fire detection

Academic literature on the topic 'Smoke and fire detection'

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Published: 25 May 2024

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Journal articles on the topic "Smoke and fire detection"

1

Dong, Wen-Hui, Xue-Er Sheng, Shu Wang, and Tian Deng. "Experimental Study on Particle Size Distribution Characteristics of Aerosol for Fire Detection." Applied Sciences 13, no. 9 (April 30, 2023): 5592. http://dx.doi.org/10.3390/app13095592.

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Current optical fire smoke detectors use scattering light intensity as an indicator of smoke concentration and trigger fire alarms when the intensity exceeds a threshold value. However, such detectors are prone to false alarms caused by non-fire aerosols since both fire smokes and non-fire aerosols scatter light. Thus, in order to reduce false alarms caused by non-fire aerosols such as dust and water vapor, fire detectors must be capable of distinguishing fire smoke from non-fire aerosols. Since the light scattering signals depend on the particle size information of aerosols, it is essential to study and characterize the particle size distribution of fire smoke and non-fire aerosols for differentiating them. In this paper, a comprehensive aerosol experimental platform is built to measure the particle size distribution of various typical fire smokes and non-fire aerosols. Through the conducted experiments, we note that there are significant differences in the particle size distributions of typical fire smokes and non-fire aerosols, with a boundary of about 1μm. The experimental results provide fundamental data support of the particle size distribution for developing a better fire detector that accurately identifies smoke.
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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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Peat, Bob. "Fire detection without smoke." Physics World 6, no. 6 (June 1993): 23–25. http://dx.doi.org/10.1088/2058-7058/6/6/18.

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Huang, Jingwen, Jiashun Zhou, Huizhou Yang, Yunfei Liu, and Han Liu. "A Small-Target Forest Fire Smoke Detection Model Based on Deformable Transformer for End-to-End Object Detection." Forests 14, no. 1 (January 16, 2023): 162. http://dx.doi.org/10.3390/f14010162.

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Forest fires have continually endangered personal safety and social property. To reduce the occurrences of forest fires, it is essential to detect forest fire smoke accurately and quickly. Traditional forest fire smoke detection based on convolutional neural networks (CNNs) needs many hand-designed components and shows poor ability to detect small and inconspicuous smoke in complex forest scenes. Therefore, we propose an improved early forest fire smoke detection model based on deformable transformer for end-to-end object detection (deformable DETR). We use deformable DETR as a baseline containing the best sparse spatial sampling for smoke with deformable convolution and relation modeling capability of the transformer. We integrate a Multi-scale Context Contrasted Local Feature module (MCCL) and a Dense Pyramid Pooling module (DPPM) into the feature extraction module for perceiving features of small or inconspicuous smoke. To improve detection accuracy and reduce false and missed detections, we propose an iterative bounding box combination method to generate precise bounding boxes which can cover the entire smoke object. In addition, we evaluate the proposed approach using a quantitative and qualitative self-made forest fire smoke dataset, which includes forest fire smoke images of different scales. Extensive experiments show that our improved model’s forest fire smoke detection accuracy is significantly higher than that of the mainstream models. Compared with deformable DETR, our model shows better performance with improvement of mAP (mean average precision) by 4.2%, APS (AP for small objects) by 5.1%, and other metrics by 2% to 3%. Our model is adequate for early forest fire smoke detection with high detection accuracy of different-scale smoke objects.
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Sun, Bingjian, Pengle Cheng, and Ying Huang. "Few-Shot Fine-Grained Forest Fire Smoke Recognition Based on Metric Learning." Sensors 22, no. 21 (November 1, 2022): 8383. http://dx.doi.org/10.3390/s22218383.

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To date, most existing forest fire smoke detection methods rely on coarse-grained identification, which only distinguishes between smoke and non-smoke. Thus, non-fire smoke and fire smoke are treated the same in these methods, resulting in false alarms within the smoke classes. The fine-grained identification of smoke which can identify differences between non-fire and fire smoke is of great significance for accurate forest fire monitoring; however, it requires a large database. In this paper, for the first time, we combine fine-grained smoke recognition with the few-shot technique using metric learning to identify fire smoke with the limited available database. The experimental comparison and analysis show that the new method developed has good performance in the structure of the feature extraction network and the training method, with an accuracy of 93.75% for fire smoke identification.
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Bhamra, Jaspreet Kaur, Shreyas Anantha Ramaprasad, Siddhant Baldota, Shane Luna, Eugene Zen, Ravi Ramachandra, Harrison Kim, et al. "Multimodal Wildland Fire Smoke Detection." Remote Sensing 15, no. 11 (May 27, 2023): 2790. http://dx.doi.org/10.3390/rs15112790.

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Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have, in turn, led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgent need to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. To that end, in this paper, we present our work on integrating multiple data sources into SmokeyNet, a deep learning model using spatiotemporal information to detect smoke from wildland fires. We present Multimodal SmokeyNet and SmokeyNet Ensemble for multimodal wildland fire smoke detection using satellite-based fire detections, weather sensor measurements, and optical camera images. An analysis is provided to compare these multimodal approaches to the baseline SmokeyNet in terms of accuracy metrics, as well as time-to-detect, which is important for the early detection of wildfires. Our results show that incorporating weather data in SmokeyNet improves performance numerically in terms of both F1 and time-to-detect over the baseline with a single data source. With a time-to-detect of only a few minutes, SmokeyNet can be used for automated early notification of wildfires, providing a useful tool in the fight against destructive wildfires.
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Bashambu, Dr Shallu, Anupam Gupta, and Sarthak Khandelwal. "Real Time Fire and Smoke Detection System." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 2593–600. http://dx.doi.org/10.22214/ijraset.2023.54039.

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Abstract: This research paper presents a real-time fire and smoke detection system using the YOLOv5 object detection algorithm. The system aims to detect fire and smoke in images and video streams captured by a camera in real-time, without the need for any preprocessing or manual intervention. The proposed system uses the YOLOv5 algorithm to detect the fire and smoke regions in the input images and videos. The YOLOv5 model is trained on a dataset of annotated images to recognize fire and smoke patterns accurately. The proposed system has been tested on different datasets and has achieved high accuracy and precision in detecting fire and smoke in real-time. The experimental results demonstrate that the proposed system is robust and efficient, and it can detect fire and smoke in real-time with high accuracy and low latency. The proposed system can be used in various applications, such as early warning systems, fire safety, and disaster management. It can also be integrated with the CCTV network directly
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Yang, Huanyu, Jun Wang, and Jiacun Wang. "Efficient Detection of Forest Fire Smoke in UAV Aerial Imagery Based on an Improved Yolov5 Model and Transfer Learning." Remote Sensing 15, no. 23 (November 27, 2023): 5527. http://dx.doi.org/10.3390/rs15235527.

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Forest fires pose severe challenges to forest management because of their unpredictability, extensive harm, broad impact, and rescue complexities. Early smoke detection is pivotal for prompt intervention and damage mitigation. Combining deep learning techniques with UAV imagery holds potential in advancing forest fire smoke recognition. However, issues arise when using UAV-derived images, especially in detecting miniature smoke patches, complicating effective feature discernment. Common deep learning approaches for forest fire detection also grapple with limitations due to sparse datasets. To counter these challenges, we introduce a refined UAV-centric forest fire smoke detection approach utilizing YOLOv5. We first enhance anchor box clustering through K-means++ to boost the classification precision and then augment the YOLOv5 architecture by integrating a novel partial convolution (PConv) to trim down model parameters and elevate processing speed. A unique detection head is also incorporated to the model to better detect diminutive smoke traces. A coordinate attention module is embedded within YOLOv5, enabling precise smoke target location and fine-grained feature extraction amidst complex settings. Given the scarcity of forest fire smoke datasets, we employ transfer learning for model training. The experimental results demonstrate that our proposed method achieves 96% AP50 and 57.3% AP50:95 on a customized dataset, outperforming other state-of-the-art one-stage object detectors while maintaining real-time performance.
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Zheng, Xin, Feng Chen, Liming Lou, Pengle Cheng, and Ying Huang. "Real-Time Detection of Full-Scale Forest Fire Smoke Based on Deep Convolution Neural Network." Remote Sensing 14, no. 3 (January 23, 2022): 536. http://dx.doi.org/10.3390/rs14030536.

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To reduce the loss induced by forest fires, it is very important to detect the forest fire smoke in real time so that early and timely warning can be issued. Machine vision and image processing technology is widely used for detecting forest fire smoke. However, most of the traditional image detection algorithms require manual extraction of image features and, thus, are not real-time. This paper evaluates the effectiveness of using the deep convolutional neural network to detect forest fire smoke in real time. Several target detection deep convolutional neural network algorithms evaluated include the EfficientDet (EfficientDet: Scalable and Efficient Object Detection), Faster R-CNN (Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks), YOLOv3 (You Only Look Once V3), and SSD (Single Shot MultiBox Detector) advanced CNN (Convolutional Neural Networks) model. The YOLOv3 showed a detection speed up to 27 FPS, indicating it is a real-time smoke detector. By comparing these algorithms with the current existing forest fire smoke detection algorithms, it can be found that the deep convolutional neural network algorithms result in better smoke detection accuracy. In particular, the EfficientDet algorithm achieves an average detection accuracy of 95.7%, which is the best real-time forest fire smoke detection among the evaluated algorithms.
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Dissertations / Theses on the topic "Smoke and fire detection"

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Lynch, James Andrew. "A study of smoke aging examining changes in smoke particulate size." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0510104-194400/.

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Saunders, Julie Ann. "The Prediction of Smoke Detector Activation Times in a Two-Storey House Fire through CFD Modelling." Thesis, University of Canterbury. Civil and Natural Resources Engineering, 2010. http://hdl.handle.net/10092/4077.

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This report describes an investigation into the prediction of the activation times of domestic ionisation and photoelectric smoke detectors within a two storey dwelling, the work undertaken being an extension to that previously presented by Brammer (2002). Three fire scenarios are considered, each having been a real test fire undertaken at the Building Research Establishment in Cardington. These three fire scenarios all involved the flaming combustion of an upholstered armchair within the lounge on the Ground floor. During the experiments various results were recorded, including temperatures, optical densities and smoke detector activation times. The fire scenarios where modelled using FDS, Version 5. Base parameters regarding the fuel load where defined to be 0.05kgsoot/kgfuel and 20MJ/kg. Consideration was also given to the effect varying the effective heat of combustion and defined soot yield would have on derived smoke detector activation times. Additional simulations where thus run considering soot yields of 0.04kgsoot/kgfuel and 0.10ksoot/kgfuel, and an effective heat of combustion of 25MJ/kg. Three prediction methods where applied to the results of the FDS simulations for derivation of the activation times of smoke detectors located throughout the house. These methods where the temperature correlation method, Heskestad’s method, and Cleary’s method. The temperature correlation method considered activation criterions of 4°C, 13°C and 20°C above ambient. The Heskestad and Cleary methods were found to derive comparable activation times for each detector location. None of the prediction algorithms where however found to predict activation times consistently comparable to the test data. Rather, it was determined that for an appropriate prediction method to be adopted for accurate assessment of a given fire scenario, consideration must be given to the: • type of detector being assessed; • location of the detector relative to the fire; • mode of combustion (i.e. flaming or smouldering); and the • growth rate of the fire.
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Alamgir, Nyma. "Computer vision based smoke and fire detection for outdoor environments." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/201654/1/Nyma_Alamgir_Thesis.pdf.

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Surveillance Video-based detection of outdoor smoke and fire has been a challenging task due to the chaotic variations of shapes, movement, colour, texture, and density. This thesis contributes to the advancement of the contemporary efforts of smoke and fire detection by proposing novel technical methods and their possible integration into a complete fire safety model. The novel contributions of this thesis include an efficient feature calculation method combining local and global texture properties, the development of deep learning-based models and a conceptual framework to incorporate weather information in the fire safety model for improved accuracy in fire prediction and detection.
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Dawod, Jakob. "Seek : More than just a smoke detector." Thesis, Umeå universitet, Designhögskolan vid Umeå universitet, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-105986.

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Every year, approximately 100 people die in fire related incidents in Sweden. ”Seek” is designed to assist fire fighters to locate people faster in smoke diving procedures. ”Seek” identifies people and possible dangers within the building before the smoke diving procedure begins. This allows the operation to be streamlined and planned, as well as avoiding risks which fire fighters are exposed to today. The early overview created by the ”Seek” smoke detector not only saves time in planning but reduces the time from accident until the people in the burning building can receive skilled care, increasing their chance of survival.
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Alsaadi, Abdulrahman. "Smart smoke and fire detection with wireless and global system for mobile technology." Thesis, California State University, Long Beach, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=1606705.

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Fire safety is one of the major concerns for a safe home environment. Current implementations of home or workplace environment monitoring systems consist of rudimentary smoke detectors devoid of any communication capabilities. Recent trends in the industry have shown a growth in the use of smart devices at homes and with the recent advances in areas of machine learning and data sciences, this trend is expected to evolve at a rate faster than ever before. These smart devices constantly monitor the data of their environment and make decisions by performing data analytics on those observations. Amazon Echo is one such example where an ‘always-listening’ device responds intelligently to a speaker’s command giving its users a Smart Home experience.

In this implementation, we harness the developments in aforementioned areas to make Smart Fire Alarm System. The Smart Fire Alarm constantly monitors the environment and not only alerts the facility where it is located, but it also communicates with the fire department and the guardian of the property through Global System for Mobile (GSM) Communication making the damage control procedures efficient and faster. An ARM7 processor (LPDC 2148), ZigBee IEEE 802.15.4 protocol, and GSM subsystems are used in this implementation to communicate between the base station and smoke detectors.

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Garges, David Casimir. "Early Forest Fire Detection via Principal Component Analysis of Spectral and Temporal Smoke Signature." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1456.

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The goal of this study is to develop a smoke detecting algorithm using digital image processing techniques on multi-spectral (visible & infrared) video. By utilizing principal component analysis (PCA) followed by spatial filtering of principal component images the location of smoke can be accurately identified over a period of exposure time with a given frame capture rate. This result can be further analyzed with consideration of wind factor and fire detection range to determine if a fire is present within a scene. Infrared spectral data is shown to contribute little information concerning the smoke signature. Moreover, finalized processing techniques are focused on the blue spectral band as it is furthest away from the infrared spectral bands and because it experimentally yields the largest footprint in the processed principal component images in comparison to other spectral bands. A frame rate of .5 images/sec (1 image every 2 seconds) is determined to be the maximum such that temporal variance of smoke can be captured. The study also shows eigenvectors corresponding to the principal components that best represent smoke and are valuable indications of smoke temporal signature. Raw video data is taken through rigorous pre-processing schemes to align frames from respective spectral band both spatially and temporally. A multi-paradigm numerical computing program, MATLAB, is used to match the field of view across five spectral bands: Red, Green, Blue, Long-Wave Infrared, and Mid-Wave Infrared. Extracted frames are aligned temporally from key frames throughout the data capture. This alignment allows for more accurate digital processing for smoke signature. v Clustering analysis on RGB and HSV value systems reveal that color alone is not helpful to segment smoke. The feature values of trees and other false positives are shown to be too closely related to features of smoke for in solely one instance in time. A temporal principal component transform on the blue spectral band eliminates static false positives and emphasizes the temporal variance of moving smoke in images with higher order. A threshold adjustment is applied to a blurred blue principal component of non-unity principal component order and smoke results can be finalized using median filtering. These same processing techniques are applied to difference images as a more simple and traditional technique for identifying temporal variance and results are compared.
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Kohler, Daniel G. "STUDY OF STATISTICAL AND COMPUTATIONAL INTELLIGENCE METHODS OF DETECTING TEMPORAL SIGNATURE OF FOREST FIRE HEAT PLUME FROM SINGLE-BAND GROUND-BASED INFRARED VIDEO." DigitalCommons@CalPoly, 2012. https://digitalcommons.calpoly.edu/theses/796.

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This thesis will analyze video from land-based, cooled mid-wave infrared cameras to identify temporal features indicative of a heat plume from a forest fire. Desirable features and methods will show an ability to distinguish between heat plume movement and other movements, such as foliage, vehicles, humans, and birds in flight. Features will be constructed primarily using combinations of statistics and principal component analysis (PCA) with intent to detect key characteristics of fire and heat plume: persistence and growth. Several classification systems will combine and filter the features in an attempt to classify pixels as either heat or non-heat. The classification systems will be tuned and compared with common metrics of error rate and computation time. It was found that the movement pattern of a heat plume could be distinguished from the similar movement pattern of foliage by detecting outlier movement patterns, a phenomenon associated with the growth property of fire. Outlier movement patterns were best detected by thresholding the quotient of mean and median of a set of variance measurements over time. The best tested classifier in terms of minimizing false positives without losing the heat signal came from PCA of a dual-range moving average difference.
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Phelan, Patrick. "Investigation of enhanced soot deposition on smoke alarm horns." Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-01075-121834/.

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Ďuriš, Denis. "Detekce ohně a kouře z obrazového signálu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-412968.

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This diploma thesis deals with the detection of fire and smoke from the image signal. The approach of this work uses a combination of convolutional and recurrent neural network. Machine learning models created in this work contain inception modules and blocks of long short-term memory. The research part describes selected models of machine learning used in solving the problem of fire detection in static and dynamic image data. As part of the solution, a data set containing videos and still images used to train the designed neural networks was created. The results of this approach are evaluated in conclusion.
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Schneider, Dirk. "Untersuchung von Methoden zur Früherkennung von Bränden in Wald- und Vegetationsgebieten." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-227018.

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Dissertation of Chief Fire Officer Dipl.-Ing. M. Sc. Dirk Schneider for achieving the academic degree of Dr.-Ing. of the Faculty of Forestry, Geo and Hydro Sciences of the Technical University of Dresden with the title: “Early Detection of Fires in Areas of Forests and other Vegetation” Fires threaten and destroy extensive forest and vegetation areas every year, endangering people and its settlements, leading to significant pressures on the environment and destroying considerable high value resources. The expenditures in manpower, logistics and finance for safety in general and fire suppression in particular are considerable. To minimize these varied and extensive consequences of fires, early detection is desirable, making an effective firefighting strategy possible. This early detection is particularly of importance in remote, large-scale areas and territories not under observation by the population, especially if they are subject to an increased or high vulnerability. After investigating and considering the causes, that repeatedly lead to forest fires not only in the Federal Republic of Germany but worldwide, the author describes different traditional and modern methods for early detection of fires in areas of forests and other vegetation. Furthermore the author develops a performance item catalog, basing on practical and economic experience, by which not only novel early warning systems can be developed, but the systems and methods described in the present study also are assessed and compared. The comparison of various early warning systems is guided not only by means of technical features, but also from an economic perspective. Financial calculation methods, staff costs and the peculiarities in public administration are particularly noted. The author also shows the different parameters that influence the selection of an appropriate early warning system for the detection of forest and vegetation areas. It becomes clear that it is the scene of the incident with its specific parameters that determines the most useful early warning system.
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Books on the topic "Smoke and fire detection"

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Bukowski, Richard. International fire detection literature review & technical analysis. Quincy, Mass: National Fire Protection Research Foundation, 1991.

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Smoke without fire. New York: Doubleday, 1989.

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Smoke without fire. Thorndike, Me: Thorndike Press, 1991.

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Olson, Karen E. Secondhand smoke. New York: Mysterious Press, 2006.

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Olson, Karen E. Secondhand Smoke. New York: Grand Central Publishing, 2006.

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National Institute of Standards and Technology (U.S.), ed. The zone fire model JET: A model for the prediction of detector activation and gas temperature in the presence of a smoke layer. Gaithersburg, Md: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1999.

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D, Davis William, and United States. National Aeronautics and Space Administration., eds. The use of computer models to predict temperature and smoke movement in high bay spaces. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1993.

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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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Smoke without fire. London: Collins, 1990.

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Book chapters on the topic "Smoke and fire detection"

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Lien, Kai-Yu, Jung-Chun Liu, Yu-Wei Chan, and Chao-Tung Yang. "Fire and Smoke Detection Using YOLO Through Kafka." In Frontier Computing on Industrial Applications Volume 4, 269–75. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9342-0_29.

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Robert Singh, A., Suganya Athisayamani, S. Sankara Narayanan, and S. Dhanasekaran. "Fire Detection by Parallel Classification of Fire and Smoke Using Convolutional Neural Network." In Computational Vision and Bio-Inspired Computing, 95–105. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6862-0_8.

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Mubeen, Muhammad, Muhammad Asad Arshed, and Hafiz Abdul Rehman. "DeepFireNet - A Light-Weight Neural Network for Fire-Smoke Detection." In Communications in Computer and Information Science, 171–81. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10525-8_14.

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Favorskaya, Margarita N., and Lakhmi C. Jain. "Deep Learning for Fire and Smoke Detection in Outdoor Spaces." In Smart Modelling for Engineering Systems, 195–209. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4619-2_15.

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Hajji, Tarik, Ibtissam El Hassani, Abdelkader Fassi Fihri, Yassine Talhaoui, and Chaimae Belmarouf. "Fire and Smoke Detection Model for Real-Time CCTV Applications." In Artificial Intelligence and Industrial Applications, 211–20. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43520-1_18.

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Guo, Xingran, Haizheng Yu, and Xueying Liao. "WCA-VFnet: A Dedicated Complex Forest Smoke Fire Detector." In Neural Information Processing, 497–508. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8073-4_38.

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Zhang, Qixing, Jia Liu, Jie Luo, Feng Wang, Jinjun Wang, and Yongming Zhang. "Characterization of Typical Fire and Non-fire Aerosols by Polarized Light Scattering for Reliable Optical Smoke Detection." In The Proceedings of 11th Asia-Oceania Symposium on Fire Science and Technology, 791–801. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9139-3_58.

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Ko, Yoon, Oluwamuyiwa Okunrounmu, Monireh Aram, and Dahai Qi. "Fire Risks of Renewable Energy Technologies in Buildings: Analysis of Fire Effluents for Smoke and Toxicant Detection." In Proceedings of the 5th International Conference on Building Energy and Environment, 1593–98. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9822-5_164.

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Zhou, Zhong, and Ya-qin Zhao. "A New Smoke Detection Method of Forest Fire Video with Color and Flutter." In Proceedings of the 2015 Chinese Intelligent Automation Conference, 151–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46469-4_16.

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Saponara, Sergio, Abdussalam Elhanashi, and Alessio Gagliardi. "Enabling YOLOv2 Models to Monitor Fire and Smoke Detection Remotely in Smart Infrastructures." In Lecture Notes in Electrical Engineering, 30–38. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66729-0_4.

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Conference papers on the topic "Smoke and fire detection"

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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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Zhaa, Xuan, Hang Ji, Dengyin Zhang, and Huanhuan Bao. "Fire Smoke Detection Based on Contextual Object Detection." In 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC). IEEE, 2018. http://dx.doi.org/10.1109/icivc.2018.8492823.

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Zhou, You, Jiaxuan Wang, Tiancheng Han, and Xuerui Cai. "Fire Smoke Detection Based on Vision Transformer." In 2022 4th International Conference on Natural Language Processing (ICNLP). IEEE, 2022. http://dx.doi.org/10.1109/icnlp55136.2022.00015.

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Wei, Yuan, Yu Chunyu, and Zhang Yongming. "Based on wavelet transformation fire smoke detection method." In Instruments (ICEMI). IEEE, 2009. http://dx.doi.org/10.1109/icemi.2009.5274409.

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Chao-Ching Ho and Tzu-Hsin Kuo. "Real-time video-based fire smoke detection system." In 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, 2009. http://dx.doi.org/10.1109/aim.2009.5229791.

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Xu, Meng, Xiuping Jia, Mark Pickering, and Dar Roberts. "Spectral unmixing for fire smoke detection and removal." In IGARSS 2016 - 2016 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2016. http://dx.doi.org/10.1109/igarss.2016.7729203.

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Grigorov, Ivan, Atanaska Deleva, Dimitar Stoyanov, Nikolay Kolev, and Georgi Kolarov. "LIDAR detection of forest fire smoke above Sofia." In Eighteenth International School on Quantum Electronics: Laser Physics and Applications, edited by Tanja Dreischuh, Sanka Gateva, and Alexandros Serafetinides. SPIE, 2015. http://dx.doi.org/10.1117/12.2178791.

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Choueiri, Samia, Daoud Daoud, Samir Harb, and Roger Achkar. "Fire and Smoke Detection Using Artificial Neural Networks." In 2020 14th International Conference on Open Source Systems and Technologies (ICOSST). IEEE, 2020. http://dx.doi.org/10.1109/icosst51357.2020.9332990.

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Jobert, Gabriel, Maryse Fournier, Pierre Barritault, Salim Boutami, Jeremie Auger, Adrien Maillard, Julien Michelot, Pierre Lienhard, Sergio Nicoletti, and Laurent Duraffourg. "A Miniaturized Optical Sensor for Fire Smoke Detection." In 2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII). IEEE, 2019. http://dx.doi.org/10.1109/transducers.2019.8808611.

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Tao, MingKun, Yang Li, ShaoPeng Li, Hao Zhuang, and Ling Li. "Early fire smoke detection model based on YOLOv5." In International Conference on Cloud Computing, Internet of Things, and Computer Applications, edited by Warwick Powell and Amr Tolba. SPIE, 2022. http://dx.doi.org/10.1117/12.2642639.

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Reports on the topic "Smoke and fire detection"

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Josephson, Alexander, and Jenna McDanold. Fire and Smoke. Office of Scientific and Technical Information (OSTI), March 2024. http://dx.doi.org/10.2172/2332767.

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Doo, Johnny. Unsettled Issues Concerning eVTOL for Rapid-response, On-demand Firefighting. SAE International, August 2021. http://dx.doi.org/10.4271/epr2021017.

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Recent advancements of electric vertical take-off and landing (eVTOL) aircraft have generated significant interest within and beyond the traditional aviation industry, and many novel applications have been identified and are in development. One promising application for these innovative systems is in firefighting, with eVTOL aircraft complementing current firefighting capabilities to help save lives and reduce fire-induced damages. With increased global occurrences and scales of wildfires—not to mention the issues firefighters face during urban and rural firefighting operations daily—eVTOL technology could offer timely, on-demand, and potentially cost-effective aerial mobility capabilities to counter these challenges. Early detection and suppression of wildfires could prevent many fires from becoming large-scale disasters. eVTOL aircraft may not have the capacity of larger aerial assets for firefighting, but targeted suppression, potentially in swarm operations, could be valuable. Most importantly, on-demand aerial extraction of firefighters can be a crucial benefit during wildfire control operations. Aerial firefighter dispatch from local fire stations or vertiports can result in more effective operations, and targeted aerial fire suppression and civilian extraction from high-rise buildings could enhance capabilities significantly. There are some challenges that need to be addressed before the identified capabilities and benefits are realized at scale, including the development of firefighting-specific eVTOL vehicles; sense and avoid capabilities in complex, smoke-inhibited environments; autonomous and remote operating capabilities; charging system compatibility and availability; operator and controller training; dynamic airspace management; and vehicle/fleet logistics and support. Acceptance from both the first-responder community and the general public is also critical for the successful implementation of these new capabilities. The purpose of this report is to identify the benefits and challenges of implementation, as well as some of the potential solutions. Based on the rapid development progress of eVTOL aircraft and infrastructures with proactive community engagement, it is envisioned that these challenges can be addressed soon. NOTE: SAE EDGE™ Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal of SAE EDGE™ Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. These reports are not intended to resolve the challenges they identify or close any topic to further scrutiny.
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McKinnon, Mark, Sean DeCrane, and Steve Kerber. Four Firefighters Injured in Lithium-Ion Battery Energy Storage System Explosion -- Arizona. UL Firefighter Safety Research Institute, July 2020. http://dx.doi.org/10.54206/102376/tehs4612.

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On April 19, 2019, one male career Fire Captain, one male career Fire Engineer, and two male career Firefighters received serious injuries as a result of cascading thermal runaway within a 2.16 MWh lithium-ion battery energy storage system (ESS) that led to a deflagration event. The smoke detector in the ESS signaled an alarm condition at approximately 16:55 hours and discharged a total flooding clean agent suppressant (Novec 1230). The injured firefighters were members of a hazardous materials (HAZMAT) team that arrived on the scene at approximately 18:28 hours. The HAZMAT team noted low-lying white clouds of a gas/vapor mixture issuing from the structure and nearby components and drifting through the desert. The team defined a hot zone and made several entries into the hot zone to conduct 360-degree size-ups around the ESS using multi-gas meters, colorimetric tubes, and thermal imaging cameras (TICs). The team detected dangerously elevated levels of hydrogen cyanide (HCN) and carbon monoxide (CO) during each entry. The team continued to monitor the ESS and noted the white gas/vapor mixture stopped flowing out of the container at approximately 19:50 hours. The HAZMAT leadership developed an incident action plan with input from a group of senior fire officers and information about the ESS provided by representatives from the companies that owned, designed, and maintained the ESS. The HAZMAT team made a final entry into the hot zone and found that HCN and CO concentrations in the vicinity of the ESS were below an acceptable threshold. In following with the incident action plan, the team opened the door to the ESS at approximately 20:01 hours. A deflagration event was observed by the firefighters outside the hot zone at approximately 20:04 hours. All HAZMAT team members received serious injuries in the deflagration and were quickly transported to nearby hospitals. Note: The lithium-ion battery ESS involved in this incident was commissioned prior to release of a first draft of the current consensus standard on ESS installations, NFPA 855 [1]; the design of the ESS complied with the pertinent codes and standards active at the time of its commissioning.
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Averill, Jason D., Erik L. Johnsson, Marc R. Nyden, Richard D. Peacock, and Richard G. Gann. Smoke component yields from room-scale fire tests. Gaithersburg, MD: National Bureau of Standards, 2003. http://dx.doi.org/10.6028/nist.tn.1453.

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Floyd, Jason E., Sean P. Hunt, Frederick W. Williams, and Patricia A. Tatem. Fire and Smoke Simulator (FSSIM) Version 1 - Theory Manual. Fort Belvoir, VA: Defense Technical Information Center, March 2004. http://dx.doi.org/10.21236/ada422214.

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Floyd, Jason E., Sean P. Hunt, Patricia A. Tatem, and Frederick W. Williams. Fire and Smoke Simulator (FSSIM) Version 1 - User's Guide. Fort Belvoir, VA: Defense Technical Information Center, July 2004. http://dx.doi.org/10.21236/ada425816.

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Averill, Jason D., Richard G. Gann, and Daniel C. Murphy. Performance of new and aged residential fire smoke alarms. Gaithersburg, MD: National Institute of Standards and Technology, 2011. http://dx.doi.org/10.6028/nist.tn.1691.

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Marsh, Nathan D., and Richard G. Gann. Smoke component yields from Bench-Scale Fire Tests : 4. Comparison with Room Fire Results. National Institute of Standards and Technology, December 2013. http://dx.doi.org/10.6028/nist.tn.1763.

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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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Jones, Walter W. Refinement of a model for fire growth and smoke transport. Gaithersburg, MD: National Bureau of Standards, 1990. http://dx.doi.org/10.6028/nist.tn.1282.

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