A microwave radiometer for detection of forest fire under conditions of insufficient visibility

Gubin, N. A.; Zolotarev, N S; Poletaev, A S; Chensky, D. A.; Batzorig, Zandan; Chensky, A.G.

doi:10.1088/1742-6596/1353/1/012092

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…Dynamic texture features were derived using two-dimensional (2D) wavelet decomposition in temporal domain and three-dimensional (3D) volumetric wavelet decomposition. Compared to optical or infrared sensors, measuring instruments working at the microwave range are more efficient for fire detection in conditions of insufficient visibility [17]. Varotsos et al [18] deployed a platform IL-18 equipped with passive microwave radiometers of 1.43, 13.3 and 37.5 GHz frequencies and proposed a decision-making system in order to achieve early detection of forest fires.…”

Section: Introductionmentioning

confidence: 99%

Early Fire Detection Based on Aerial 360-Degree Sensors, Deep Convolution Neural Networks and Exploitation of Fire Dynamic Textures

et al. 2020

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The environmental challenges the world faces have never been greater or more complex. Global areas that are covered by forests and urban woodlands are threatened by large-scale forest fires that have increased dramatically during the last decades in Europe and worldwide, in terms of both frequency and magnitude. To this end, rapid advances in remote sensing systems including ground-based, unmanned aerial vehicle-based and satellite-based systems have been adopted for effective forest fire surveillance. In this paper, the recently introduced 360-degree sensor cameras are proposed for early fire detection, making it possible to obtain unlimited field of view captures which reduce the number of required sensors and the computational cost and make the systems more efficient. More specifically, once optical 360-degree raw data are obtained using an RGB 360-degree camera mounted on an unmanned aerial vehicle, we convert the equirectangular projection format images to stereographic images. Then, two DeepLab V3+ networks are applied to perform flame and smoke segmentation, respectively. Subsequently, a novel post-validation adaptive method is proposed exploiting the environmental appearance of each test image and reducing the false-positive rates. For evaluating the performance of the proposed system, a dataset, namely the “Fire detection 360-degree dataset”, consisting of 150 unlimited field of view images that contain both synthetic and real fire, was created. Experimental results demonstrate the great potential of the proposed system, which has achieved an F-score fire detection rate equal to 94.6%, hence reducing the number of required sensors. This indicates that the proposed method could significantly contribute to early fire detection.

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Section: Introductionmentioning

confidence: 99%

Early Fire Detection Based on Aerial 360-Degree Sensors, Deep Convolution Neural Networks and Exploitation of Fire Dynamic Textures

et al. 2020

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“…Compared with infrared radiation detectors, Gubin et al [3] believe that the super high frequency radiometer is more effective when thick tree crowns, meteorological environment and dense smoke are all considered.…”

Section: Introductionmentioning

confidence: 99%

Using PCAand one‐stage detectors for real‐time forest fire detection

Guo

Yang

2020

J. eng.

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Design and Implementation of IoT based Multi Degree Rotating Fire Extinguisher System

Venusamy,

et al. 2023

2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC)

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A microwave radiometer for detection of forest fire under conditions of insufficient visibility

Cited by 3 publications

References 2 publications

Early Fire Detection Based on Aerial 360-Degree Sensors, Deep Convolution Neural Networks and Exploitation of Fire Dynamic Textures

Early Fire Detection Based on Aerial 360-Degree Sensors, Deep Convolution Neural Networks and Exploitation of Fire Dynamic Textures

Using PCAand one‐stage detectors for real‐time forest fire detection

Design and Implementation of IoT based Multi Degree Rotating Fire Extinguisher System

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