【摘要】：火災(zāi)這一自然災(zāi)害對(duì)于人們?nèi)粘５纳a(chǎn)生活來(lái)說(shuō)具有非常大的破壞力,同時(shí)也對(duì)人民群眾的財(cái)產(chǎn)安全和生命安全造成了嚴(yán)重的威脅。通常火災(zāi)發(fā)生的初期破壞力不大,容易被忽視,但其發(fā)展迅速,隨時(shí)間推移,破壞力迅速增長(zhǎng),因此越早發(fā)現(xiàn)火情并將其消除,所遭受的損失就越小。傳統(tǒng)的火情監(jiān)測(cè)方法一般依賴于人工巡檢,其成本巨大,且在某些特殊危險(xiǎn)區(qū)域無(wú)法使用;而另外一種更廣泛的火情監(jiān)測(cè)方法是基于多種傳感器技術(shù):將感煙、感光、感氣等傳感器與網(wǎng)絡(luò)技術(shù)結(jié)合,通過(guò)對(duì)監(jiān)測(cè)場(chǎng)所的物理和化學(xué)物質(zhì)進(jìn)行遠(yuǎn)程監(jiān)測(cè)分析來(lái)實(shí)現(xiàn)火情監(jiān)測(cè)。這種方法雖然克服了人工巡檢的缺點(diǎn),但是容易受環(huán)境影響,難以勝任開闊場(chǎng)所的監(jiān)測(cè)任務(wù)。近年來(lái),隨著數(shù)字圖像處理技術(shù)以及計(jì)算機(jī)視覺研究領(lǐng)域的不斷發(fā)展,融合了數(shù)字圖像處理和計(jì)算機(jī)視覺技術(shù)的基于視頻的火情監(jiān)測(cè)技術(shù)由于成本低廉、適用范圍廣等優(yōu)點(diǎn)吸引了國(guó)內(nèi)外大量的專家學(xué)者,成為研究熱點(diǎn)。本文主要研究分析了傳統(tǒng)火情監(jiān)測(cè)技術(shù)的研究現(xiàn)狀以及不足之處,并在對(duì)視頻序列中的火焰圖像在空間和時(shí)間上的特征進(jìn)行分析后,提出了運(yùn)用數(shù)字圖像處理和計(jì)算機(jī)視覺處理技術(shù)的基于視頻特征的火情監(jiān)測(cè)方法。具體內(nèi)容如下:首先針對(duì)視頻中運(yùn)動(dòng)火焰前景提取的需求提出一種改進(jìn)的ViBe(visual background extractor)算法。將亮度特征匹配引入背景模型更新策略,以此來(lái)解決原始ViBe算法無(wú)法區(qū)分運(yùn)動(dòng)火焰和其他運(yùn)動(dòng)物體,且在光線變化時(shí)容易誤檢測(cè)的問(wèn)題;同時(shí),利用幀間差分法計(jì)算速度快的特點(diǎn)預(yù)提取出畫面中的運(yùn)動(dòng)區(qū)域以及背景作為初始化幀,再使用ViBe算法對(duì)該區(qū)域做進(jìn)一步更準(zhǔn)確的前景提取,改善了ViBe算法實(shí)時(shí)性隨分辨率增大而下降,且初始化幀選取不當(dāng)時(shí)易出現(xiàn)檢測(cè)鬼影的不足。其次對(duì)提取出的前景區(qū)域進(jìn)行火焰顏色特征匹配和火焰形態(tài)特征匹配。提出了基于正序數(shù)比值的火焰尖角程度匹配模型;并在分析火焰區(qū)域面積增長(zhǎng)性的特征后設(shè)計(jì)了一種基于面積增長(zhǎng)閾值的檢測(cè)報(bào)警算法。最后,論文基于Qt和OpenCv開發(fā)了火情監(jiān)測(cè)系統(tǒng)軟件,對(duì)提出的相關(guān)算法進(jìn)行了驗(yàn)證。實(shí)驗(yàn)表明,本系統(tǒng)能夠較好的識(shí)別出視頻中的火焰信息,具有良好的工程應(yīng)用前景。
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圖片說(shuō)明：中值濾波常用窗口Fig2-2Commonwindowsinmediumfilter
[Abstract]:Fire, a natural disaster, is very destructive to people's daily production and life, and also poses a serious threat to the property safety and life safety of the people. Usually, the initial destructive power of fire is not great and easy to be ignored, but its development is rapid. With the passage of time, the destructive power increases rapidly, so the sooner the fire is discovered and eliminated, the smaller the loss will be. The traditional fire monitoring method generally depends on manual inspection, which is costly and can not be used in some special dangerous areas. Another more extensive fire monitoring method is based on a variety of sensor technologies: combining smoke sensing, photosensory, gas sensing and other sensors with network technology to achieve fire monitoring by remote monitoring and analysis of physical and chemical substances in the monitoring place. Although this method can overcome the shortcomings of manual inspection, it is easy to be affected by the environment and is difficult to be competent for the monitoring task of open places. In recent years, with the continuous development of digital image processing technology and computer vision research field, video-based fire monitoring technology, which combines digital image processing and computer vision technology, has attracted a large number of experts and scholars at home and abroad because of its low cost and wide application range. In this paper, the research status and shortcomings of traditional fire monitoring technology are studied and analyzed. After analyzing the spatial and temporal characteristics of flame images in video sequences, a fire monitoring method based on video features using digital image processing and computer vision processing technology is proposed. The main contents are as follows: firstly, an improved ViBe (visual background extractor) algorithm is proposed to meet the requirements of moving flame foreground extraction in video. The brightness feature matching is introduced into the background model updating strategy to solve the problem that the original ViBe algorithm can not distinguish moving flame from other moving objects, and it is easy to misdetect when the light changes. At the same time, the moving region and background in the picture are taken as the initial frame by using the inter-frame difference method to calculate the fast speed, and then the ViBe algorithm is used to extract the foreground more accurately, which improves the real-time performance of ViBe algorithm decreases with the increase of resolution, and the lack of ghost detection is easy to occur when the initial frame is not selected. Secondly, flame color feature matching and flame morphological feature matching are carried out for the extracted foreground region. A flame angle matching model based on positive ordinal ratio is proposed, and a detection and alarm algorithm based on area growth threshold is designed after analyzing the characteristics of flame area growth. Finally, the fire monitoring system software is developed based on Qt and OpenCv, and the related algorithms are verified. The experimental results show that the system can better identify the flame information in the video and has a good engineering application prospect.
【學(xué)位授予單位】：江西農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：X932;TP391.41

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