Application of neural network to analyses of CCD colour TV-camera image for the detection of car fires in expressway tunnels

Ono, Takiyoshi; Ishii, Hirotake; Kawamura, Kousei; Miura, Hitoshi; Momma, Eiichiro; Fujisawa, Tetsuya; Hozumi, Junichi

doi:10.1016/j.firesaf.2005.06.006

Cited by 27 publications

(12 citation statements)

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“…Fire tests together with movies of fire tests or accidents are an effective way to provide experience to the operators. The fire test evaluation from the operators can be supported or substituted by expert systems [17]. This procedure can be directly integrated with the proposed control system, as fuzzy logic is an effective tool also for the elaboration of fire images.…”

Section: Article In Pressmentioning

confidence: 99%

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A fuzzy logic procedure for ventilation control in case of fire in long tunnels

Borchiellini

Verda

2009

Fire Safety Journal

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Section: Article In Pressmentioning

confidence: 99%

“…(15)- (17). Branches are identified by a conventionally chosen flow direction and by two nodes that represent the entrance and exit sections.…”

Section: Article In Pressmentioning

confidence: 99%

A fuzzy logic procedure for ventilation control in case of fire in long tunnels

Borchiellini

Verda

2009

Fire Safety Journal

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“…and visualized a three-dimensional (3D) model of the fires [9,10]. For tunnel fires, color and motion information in the surveillance images is used for the early detection of an event and to minimize false alarms [11,12]. Indoor fires need further analysis, since there are fire-like objects that show up on the video screen.…”

Section: Introductionmentioning

confidence: 99%

Fire recognition based on correlation of segmentations by image processing techniques

Chen

Lee

2015

Machine Vision and Applications

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This article provides a novel method to recognize fires in areas monitored by a color video camera. Formulated with a graphical programming language, the developed software utilizes the HSI attributes to extract, statically and dynamically, the pixels of flames with high intensity (I value) as well as those of peri-flame regions within a specific range of saturation (S value). The other pixels that are filtered out are regarded as the backgrounds. Some sample clips of fires and pseudo fires are processed with the software in which the high intensity and the specific saturation regions can be effectively segmented from the images. For each fire clip, after analyzing the areas of foreground pixels for both I value and S value along the time axis, there exists a high correlation between both sequences. The pseudo fire clips, on the other hand, do not demonstrate high correlations after the same processing. Therefore, a fire incident can be identified according to the correlation of both extracted pixel areas. This promising achievement has laid down a firm basis for the development of a novel fire detecting alarm system in the near future.

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“…There are also studies to detect fire in tunnels using models including neural-network [7], a statistical color-model [8], and studies to detect smoke by a color-model and smoke expansion character [9]. However, the authors of these articles had problems related to the training period, real-time processing burden of complex calculation, and the possibility of color-shifts by lights in tunnels.…”

Section: Introductionmentioning

confidence: 99%