消防外文翻譯--使用ip攝像機(jī)的早期火災(zāi)探測(編輯修改稿)
2025-06-25 21:47 本頁面
【文章內(nèi)容簡介】 ]. To provide more accurate and reliable smoke detection, some video processingbased detection systems have been proposed. Generally the video processingbased fire detection algorithms are carried out using two principal characteristics of fire, which are flame and smoke. Almost all fire detection algorithms in the literature perform a pixel level analysis using some flame and/or smoke properties, such as the flame/smoke color, flickering nature, loss of background edges in frames, among others. In [2], authors proposed a method for fire detection using a multilayer neural work (MNN) with a backpropagation algorithm, which is trained using the color property of flames presented in the HSI (HueSaturationIntensity) color space. This algorithm analyses the color of each pixel to determine if some pixels present the flame features or not. In [3] and [4], the Hidden Markov Models (HMM) and the discrete wavelet transform (DWT) are used to detect flickering pixels that indicate the presence of flames. Generally the presence of flames may indicate more a serious fire situation than the presence of smoke only. Therefore for early fire detection purposes, smoke detection schemes may be more efficient. In [5] and [6], the authors use of a method for detecting smoke based on the loss of high frequencies using HMM and DWT. In [1] the RGB image sequences are analyzed to detect smoke using its chromaticity and grade of disorder. The proposal of [7] bines several dynamic and static smoke features, such as growth, disorder, flicking frequency and the energy of wavelet transform, and then this bined information is used to train a MNN to detect the presence of smoke. In [8], a smoke detection algorithm analyses the smoke candidate area using the smoke motion direction in a cumulative manner through the video sequences. The algorithm in [9] seeks 8 to detect the smoke and the flame inside a tunnel, in which the fire detection is based on the extracted motion area using a background image and the motion history of images, as well as the invariant moments. The main problem of this application is the large amount of movement generated by cars and heavy air currents. In the smoke detection algorithm proposed by [10], the smoke is considered as a type of texture pattern, which is extracted using local binary patterns (LBP) that are monly used as texture classifier. These LBP are then used to train a MNN which determines the presence of smoke. In [11], using the smoke color property defined in [1] and smoke motion detected by optical flow algorithm, a MNN is trained to detect the presence of smoke. It is worth noting that all fire detection algorithms mentioned above operate in the spatial domain, analyzing pixel values of each frame of video. Recently the use of IP cameras in video surveillance has grown significantly, because video surveillance systems based on IP technology are easy to implement at low cost due to the use of cabling and wireless Inter infrastructure already present in many panies [12]. Moreover, an IP camera not only captures sequences of images, but also has its own processor, memory and operating system, allowing loaded programs to process the captured information without the need of additional puter equipment. IP cameras can also be connected to form works, making a video surveillance system more reliable. Generally the information provided by IP camera is encode
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