Real-time video-based fire smoke detection system

Ho, Chao-Ching; Kuo, Tzu-Hsin

doi:10.1109/aim.2009.5229791

Cited by 21 publications

(2 citation statements)

References 10 publications

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“…Hence, smoke orientation is accumulated over time to compensate for inaccuracy. A fuzzy logic method is employed to detect smoke in a real-time alarm system in Ho and Kuo (2009). Spectral, spatial, and temporal features are used for extracting smoke and for helping with the validation of smoke.…”

Section: Fire Detection With Visual Imagesmentioning

confidence: 99%

A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques

2015

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Because of their rapid maneuverability, extended operational range, and improved personnel safety, unmanned aerial vehicles (UAVs) with vision-based systems have great potential for monitoring, detecting, and fighting forest fires. Over the last decade, UAV-based forest fire fighting technology has shown increasing promise. This paper presents a systematic overview of current progress in this field. First, a brief review of the development and system architecture of UAV systems for forest fire monitoring, detection, and fighting is provided. Next, technologies related to UAV forest fire monitoring, detection, and fighting are briefly reviewed, including those associated with fire detection, diagnosis, and prognosis, image vibration elimination, and cooperative control of UAVs. The final section outlines existing challenges and potential solutions in the application of UAVs to forest firefighting.Résumé : Étant donné qu'ils sont rapidement manoeuvrables, qu'ils ont un grand rayon d'action opérationnel et qu'ils offrent une meilleure sécurité pour le personnel, les véhicules aériens sans pilote (UAV) équipés de systèmes de vision ont un potentiel énorme pour surveiller, détecter et combattre les feux de forêt. Au cours de la dernière décennie, la technologie de lutte contre les feux de forêt qui utilise des UAV s'est avérée de plus en plus prometteuse. Cet article présente un aperçu complet des progrès actuels dans ce domaine. D'abord, une brève revue du développement et de l'architecture des systèmes de surveillance, de détection et de combat des feux de forêt qui utilisent des UAV est présentée. Ensuite, les technologies reliées à la surveillance, à la détection et au combat des feux de forêt sont brièvement passées en revue, incluant celles qui sont associées à la détection, au diagnostic et au pronostic des feux, à l'élimination de la vibration des images et au contrôle coopératif des UAV. La dernière section décrit les défis actuels et les solutions potentielles liés à l'utilisation des UAV dans la lutte contre les feux de forêt. [Traduit par la Rédaction] Mots-clés : feu de forêt, surveillance, détection et combat des feux, traitement des images, télédétection, véhicules aériens sans pilote.

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Section: Fire Detection With Visual Imagesmentioning

confidence: 99%

A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques

2015

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“…Traditional methods typically rely on underlying image features such as morphology, colour, and texture. For example, a robust accelerated feature that reflects texture characteristics [ 4 ], shape context that represents contour shape [ 5 , 6 , 7 , 8 ], sparse coding based on visual feature construction [ 9 , 10 ], background contrast object detection dependent on optical flow difference and wavelet variation [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ], and Gabor filters for extracting object edge features for texture analysis [ 20 , 21 , 22 ]. While manually designed feature-based algorithms can detect fires to a certain extent, they are often hardware- and environment-intensive.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Fire Smoke Detection Method Combining a Self-Attention Mechanism and Radial Multi-Scale Feature Connection

Jin

Zheng

et al. 2023

Sensors

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Fire remains a pressing issue that requires urgent attention. Due to its uncontrollable and unpredictable nature, it can easily trigger chain reactions and increase the difficulty of extinguishing, posing a significant threat to people’s lives and property. The effectiveness of traditional photoelectric- or ionization-based detectors is inhibited when detecting fire smoke due to the variable shape, characteristics, and scale of the detected objects and the small size of the fire source in the early stages. Additionally, the uneven distribution of fire and smoke and the complexity and variety of the surroundings in which they occur contribute to inconspicuous pixel-level-based feature information, making identification difficult. We propose a real-time fire smoke detection algorithm based on multi-scale feature information and an attention mechanism. Firstly, the feature information layers extracted from the network are fused into a radial connection to enhance the semantic and location information of the features. Secondly, to address the challenge of recognizing harsh fire sources, we designed a permutation self-attention mechanism to concentrate on features in channel and spatial directions to gather contextual information as accurately as possible. Thirdly, we constructed a new feature extraction module to increase the detection efficiency of the network while retaining feature information. Finally, we propose a cross-grid sample matching approach and a weighted decay loss function to handle the issue of imbalanced samples. Our model achieves the best detection results compared to standard detection methods using a handcrafted fire smoke detection dataset, with APval reaching 62.5%, APSval reaching 58.5%, and FPS reaching 113.6.

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A Survey on Video Smoke Detection

Matlani

Shrivastava

2017

Information and Communication Technology for Sustainable Development

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Real-time video-based fire smoke detection system

Cited by 21 publications

References 10 publications

A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques

A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques

Real-Time Fire Smoke Detection Method Combining a Self-Attention Mechanism and Radial Multi-Scale Feature Connection

A Survey on Video Smoke Detection

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