【摘要】：基于視頻圖像煙霧檢測技術(shù)相對基于傳感器原理的煙霧探測技術(shù)具有受環(huán)境影響小、響應(yīng)速度快以及檢測結(jié)果直觀等優(yōu)勢,是實現(xiàn)火災(zāi)早期預(yù)警的重要手段�；馂�(zāi)發(fā)生初期,通常伴隨著煙霧的產(chǎn)生,本文的主要研究工作是通過監(jiān)控場景來判斷是否有煙霧產(chǎn)生。現(xiàn)有的煙霧識別方法均是從圖像中直接提取煙霧的可視化特征,所提取的特征包含背景和煙霧兩部分信息,致使無法有效描述煙霧的特征,從而影響了煙霧識別的精度。針對該問題,本文從成像原理的角度出發(fā),認(rèn)為一幅圖像是由背景圖像與煙霧圖像線性混合而成的,提出煙霧線性表達(dá)模型及其最優(yōu)化問題,并提出成分分離算法求解該問題。成分分離算法,即通過從當(dāng)前圖像中單獨分離出煙霧成分后,提取其紋理特征進而實現(xiàn)煙霧識別。該算法以矩形塊為計算單位,根據(jù)相鄰像素間像素具有相似性,構(gòu)建局部平滑模型;同時,從整個紋理結(jié)構(gòu)的角度看,純煙霧圖像位于一個低維的子空間,并且可以采用主成分分析來確定純煙霧圖像的子空間進而描述純煙霧圖像,因此,構(gòu)建主成分模型。通過采用這兩個模型對圖像進行成分分離,得到純煙霧成分,然后利用LBP算子提取其紋理特征,最后代入支持向量機分類器中判斷煙霧是否存在,從而實現(xiàn)煙霧識別。通過合成圖像以及真實視頻數(shù)據(jù)對本文提出的算法性能進行評估,從檢測精度方面與Toreyin和Tian煙霧識別算法進行對比分析。實驗結(jié)果表明,該算法在室內(nèi)、室外、背景復(fù)雜的情況下均能有效識別出煙霧,正檢率均在93%以上,誤檢率以及漏檢率均在在4%以下;同時,針對全覆蓋濃煙、全覆蓋薄煙、局部覆蓋度大于50%的煙、局部覆蓋度小于50%的煙四類不同類型的煙霧圖像,本文算法平均精度高于Toreyin和Tian煙霧識別算法,平均檢測精度為91.3%,誤檢率為5.4%,漏檢率為3.3%。
[Abstract]:Compared with the smoke detection technology based on sensor principle, the video image smoke detection technology has the advantages of less environmental impact, fast response and intuitive detection results. It is an important means to achieve early warning of fire. In the early stage of fire, smoke is usually accompanied by smoke. The main work of this paper is to judge whether smoke is produced by monitoring scene. The existing methods of smoke recognition are to extract the visual features of smoke directly from the image. The extracted features include background and smoke information, which can not effectively describe the characteristics of smoke, thus affecting the accuracy of smoke recognition. In this paper, from the angle of imaging principle, we think that an image is a linear mixture of background image and smoke image, propose a smoke linear representation model and its optimization problem, and propose a component separation algorithm to solve the problem. The component separation algorithm is to extract the texture feature of the smoke component from the current image and then to realize the smoke recognition by separating the smoke component separately from the current image. The algorithm takes the rectangular block as the unit of calculation and constructs a local smoothing model according to the pixel similarity between adjacent pixels. At the same time, from the point of view of the whole texture structure, the pure smog image is located in a low-dimensional subspace. And the principal component analysis can be used to determine the subspace of the pure smoke image and then describe the pure smoke image. Therefore, the principal component model is constructed. By using these two models to separate the components of the image, the pure smoke components are obtained, and then the texture features are extracted by using the LBP operator. Finally, the smoke is judged in the support vector machine classifier to realize smoke recognition. The performance of the proposed algorithm is evaluated by synthetic images and real video data, and compared with Toreyin and Tian smoke recognition algorithms in terms of detection accuracy. The experimental results show that the algorithm can effectively identify smoke in indoor, outdoor and complex background, the positive detection rate is over 93%, the false detection rate and missed detection rate are below 4%. The average accuracy of this algorithm is higher than that of Toreyin and Tian smoke recognition algorithms. The average detection accuracy is 91.3%, the false detection rate is 5.4%, and the missed detection rate is 3.3%.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 龐志勇;譚洪舟;陳弟虎;;一種改進的低成本自適應(yīng)雙三次插值算法及VLSI實現(xiàn)[J];自動化學(xué)報;2013年04期

2 王濤;劉淵;謝振平;;一種基于飄動性分析的視頻煙霧檢測新方法[J];電子與信息學(xué)報;2011年05期

3 曹丙花;范孟豹;荊勝羽;;基于太赫茲波時域光譜技術(shù)的紡織材料鑒別和分類技術(shù)研究[J];光譜學(xué)與光譜分析;2010年07期

4 王昊京;王建立;王鳴浩;陰玉梅;;采用雙線性插值收縮的圖像修復(fù)方法[J];光學(xué)精密工程;2010年05期

5 練秋生;李黨;;融合多種特征的煙霧圖像檢測算法[J];光學(xué)技術(shù);2009年04期

6 袁非牛;張永明;劉士興;于春雨;沈詩林;;基于累積量和主運動方向的視頻煙霧檢測方法[J];中國圖象圖形學(xué)報;2008年04期

7 帥師;周平;汪亞明;周維達(dá);;基于小波的實時煙霧檢測[J];計算機應(yīng)用研究;2007年03期

8 宋巨龍,錢富才,彭剛;利用平面上的黃金分割法求全局最優(yōu)解[J];數(shù)學(xué)的實踐與認(rèn)識;2004年11期

9 鄧乃揚,諸梅芳;關(guān)于Powell方法理論基礎(chǔ)的探討[J];北京工業(yè)大學(xué)學(xué)報;1979年02期

10 吳方;關(guān)于Powell方法的一個注[J];數(shù)學(xué)學(xué)報;1977年01期

相關(guān)博士學(xué)位論文 前2條

1 郭艷菊;基于仿生智能優(yōu)化的圖像處理算法研究[D];河北工業(yè)大學(xué);2014年

2 于春雨;基于光流法火災(zāi)煙霧視頻圖像識別及多信息融合探測算法研究[D];中國科學(xué)技術(shù)大學(xué);2010年

相關(guān)碩士學(xué)位論文 前1條

1 涂尚斌;基于視頻圖像序列的目標(biāo)檢測及跟蹤算法研究[D];西安電子科技大學(xué);2013年

，

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