Heterogeneous sensor arrays: Merging cameras and gas sensors into innovative fire detection systems

Hackner, Angelika; Oberpriller, Helmut; Ohnesorge, Alexander; Hechtenberg, Volker; Müller, Gerhard

doi:10.1016/j.snb.2016.02.081

Cited by 19 publications

(6 citation statements)

References 8 publications

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“…This method provides higher accuracy and sensitivity compared to other common machine learning algorithms. But due to level of active fires in small region, this algorithm could not detect active fires (Hackner et al, 2016;Hastie et al, 2009;Jia et al, 2016;Kapil et al, 2016;Kecman et al, 2005;Koltunov et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

An Optimum Classifier Model with Fuzzy C-Means for Fire Detection Technology

Gandhi¹,

Narayanaswamy²

2023

JST

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Flames recognition methodology is most important for completely diminishing the flame losses in different fired environmental conditions. However, there is delayed detection and lower accuracy in the various common detection methods. Thus, optimum image/video fire detection technology is proposed in this paper based on a support vector machine (SVM) with the fuzzy c-mean, discrete wavelet transform (DWT), and gray level co-occurrence matrices (GLCM) feature extraction for the detection of fires. This algorithm has been tested on various fire and non-fire images for classification accuracy. A performance evaluation of the proposed classifier algorithm and existing algorithms is compared, showing that the accuracy and other metrics of the proposed classifier algorithm are higher than other algorithms. Furthermore, simulation results show that the proposed classifier model is improved the forecast detection accuracy of fires.

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Section: Related Workmentioning

confidence: 99%

An Optimum Classifier Model with Fuzzy C-Means for Fire Detection Technology

Gandhi¹,

Narayanaswamy²

2023

JST

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“…These features make fire detection with imagery possible. Thus, many scholars have studied fire recognition methods, but fire detection can still be improved [9], [10]. In practical applications, many problems are studied due to some restrictions.…”

Section: Related Workmentioning

confidence: 99%

Fire Detection and Recognition Optimization Based on Virtual Reality Video Image

Xin-chu

2020

IEEE Access

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Fire detection technology based on video images can avoid many flaws in conventional methods and detect fires. To achieve this, the support vector machine (SVM) method in machine learning theory has unique advantages, while rough set (RS) theory and SVM complement each other in application. Thus, a new classifier could be created by organically combining these methods to identify fires and provide fire warnings, yielding excellent noise suppression and promotion. Therefore, in this study, an RS is used as the front-end system for the SVM method, yielding improved performance than only SVM. Recognition time is reduced, and recognition efficiency is improved. Experiments show that the RS-SVM classifier model based on parameter optimization proposed in this paper mitigates deficiencies in overfitting and determining local extremum with excellent reliability and stability, and enhances the forecast accuracy of fires. The method also reduces false fire-detection alarms and uses fire feature selection in virtual reality (VR) video images and fire detection and recognition.

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“…UxVs are usually utilized complementary and synthetically to static area sensors; when needed. Since both monolithic approaches have advantages and disadvantages, several studies focus on the symbiosis of the two heterogeneous types of remote sensing (mobile and static) [10,34]. On the same matter, the current study focuses on a gas plume tracking simulative application where static and remote sensing platforms are called to form a symbiotic network of heterogeneous yet collaborative elements where both are exploited according to their particular operational advantages.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Plume Tracking Utilizing Symbiotic Heterogeneous Remote Sensing Platforms

Michailidis

Kapoutsis

Kosmatopoulos

et al. 2021

IFIP Advances in Information and Communication Technology

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The current study focuses on the problem of continuously tracking a dynamically evolving CH4 plume utilizing a mutually built consensus by heterogeneous sensing platforms: mobile and static sensors. Identifying the major complexities and emergent dynamics (leakage source, intensity, time) of such problem, a distributed, multi-agent, optimization algorithm was developed and evaluated in an indoor continuous plume-tracking application (where reaction time is critical due to the limited volume available for air saturation by the CH4 dispersion). The high-fidelity ANSYS Fluent suite realistic simulation environment was used to acquire the gas diffusion evolution through time. The analysis of the simulation results indicated that the proposed algorithm was capable of continuously readapting the mobile sensing platforms formation according to the density and the dispersed volume plume; combining additive information from the static sensors. Moreover, a scalability analysis with respect to the number of mobile platforms revealed the flexibility of the proposed algorithm to different numbers of available assets.

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Heterogeneous sensor arrays: Merging cameras and gas sensors into innovative fire detection systems

Cited by 19 publications

References 8 publications

An Optimum Classifier Model with Fuzzy C-Means for Fire Detection Technology

An Optimum Classifier Model with Fuzzy C-Means for Fire Detection Technology

Fire Detection and Recognition Optimization Based on Virtual Reality Video Image

Dynamic Plume Tracking Utilizing Symbiotic Heterogeneous Remote Sensing Platforms

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