本文選題：背景動態(tài)更新 + 暗通道先驗��； 參考：《華僑大學》2017年碩士論文

【摘要】：火災是嚴重危害人類生命財產(chǎn)安全和自然生態(tài)環(huán)境的重大災害之一�；馂牡募皶r預警對于減少各項損失意義重大。一般火災發(fā)生初期火焰較小,但是煙霧卻很明顯,因此對火災煙霧的檢測是及時判斷火災是否發(fā)生的重要依據(jù)。傳統(tǒng)的火災檢測技術依賴傳感器工作,在開放空間中的使用受到限制。隨著智能監(jiān)控設備的普及,基于視頻圖像的火災煙霧檢測技術受到廣泛關注,它可以有效避免部分環(huán)境因素產(chǎn)生的影響,且在大尺度空間監(jiān)測上具有明顯的優(yōu)勢。本文提出了一種動態(tài)檢測和靜態(tài)分類相結(jié)合的基于視頻圖像的火災煙霧檢測方法,分別從候選煙霧區(qū)域提取和圖像特征提取與分類等方面重點研究了火災煙霧檢測算法,主要的工作包括:(1)提出一種基于背景動態(tài)更新與暗通道先驗的火災煙霧檢測算法。首先通過改進的背景動態(tài)更新算法提取運動前景,解決了傳統(tǒng)運動目標檢測算法針對擴散緩慢的煙霧做前景檢測時,容易出現(xiàn)的空洞現(xiàn)象;然后針對目前算法在復雜環(huán)境下適應性不強的問題,例如自然場景中存在著諸如樹枝晃動、行人和其他運動物體的干擾,很容易產(chǎn)生誤檢,提出一種基于暗通道先驗知識的干擾物體過濾方法,該方法結(jié)合運動目標檢測算法,使其在候選煙霧區(qū)域提取階段就可以消除多數(shù)干擾物體;最后通過多特征融合的方式實現(xiàn)分類識別。實驗結(jié)果表明算法可以有效減少誤檢,提升檢測性能。(2)提出一種基于卷積神經(jīng)網(wǎng)絡的火災煙霧檢測算法。由于煙霧沒有固定的顏色和輪廓,傳統(tǒng)基于手工設計特征的煙霧檢測算法難以描述煙霧的本質(zhì)屬性,進而影響檢測的準確性;同時手工設計和處理特征需要一定的專業(yè)知識和經(jīng)驗,這些因素給火災煙霧檢測研究帶來難度。因此,本文在前面研究的基礎上提出一種基于卷積神經(jīng)網(wǎng)絡的火災煙霧檢測算法,算法通過多層的網(wǎng)絡結(jié)構能夠自動地學習更具判別性的高層特征。高層特征使得算法對于目標的表觀變化具有一定的魯棒性,適合煙霧這類變化物體的特征提取。實驗表明通過采取的隱含擴大候選區(qū)域策略結(jié)合深度卷積神經(jīng)網(wǎng)絡強大的特征抽取能力,大大提高了視頻煙霧檢測的準確性和及時性。
[Abstract]:Fire is one of the major hazards that seriously endangers the safety of human life and property and natural ecological environment. The timely warning of fire is of great significance to reduce the loss of all kinds of losses. The fire is very small at the beginning of the fire, but the smoke is very obvious. So the detection of fire smoke is an important basis for timely judgment of the occurrence of fire. Disaster detection technology relies on sensor work and is limited in the use of open space. With the popularization of intelligent monitoring equipment, fire smoke detection technology based on video image has been widely concerned. It can effectively avoid the impact of some environmental factors and has obvious advantages on large scale space monitoring. A fire smoke detection method based on video image based on dynamic detection and static classification. The fire smoke detection algorithms are studied from the extraction of candidate smoke region and image feature extraction and classification. The main work includes: (1) a fire smoke based on the background dynamic update and the dark channel prior is proposed. Detection algorithm. Firstly, the motion foreground is extracted by the improved background dynamic updating algorithm, which solves the cavitation phenomenon which is easy to appear when the traditional moving target detection algorithm is easy to detect in the foreground detection of the slow diffused smoke, and then to the problem of poor adaptability in the complex environment, such as the natural scene, such as the branch. The interference of sloshing, pedestrians and other moving objects is easy to be misdiagnosed. A filtering method based on the prior knowledge of dark channels is proposed. This method combines the moving target detection algorithm to eliminate most of the interference objects in the extraction phase of the candidate smoke region; finally, the classification recognition is realized by multi feature fusion. The experimental results show that the algorithm can effectively reduce the error detection and improve the detection performance. (2) a fire smoke detection algorithm based on convolution neural network is proposed. Because the smoke does not have fixed color and contour, the traditional smoke detection algorithm based on manual design features is difficult to describe the essential properties of the smoke and then affects the accuracy of the detection. At the same time, the manual design and processing features require certain professional knowledge and experience. These factors bring difficulty to the fire smoke detection and research. Therefore, a fire smoke detection algorithm based on the convolution neural network is proposed on the basis of the previous research. The algorithm can learn more discriminant by the multi-layer network structure. The high level feature makes the algorithm have a certain robustness to the apparent change of the target, and is suitable for the feature extraction of the change objects such as smoke. The experiment shows that the accuracy of the video smoke detection is greatly improved by combining the implicit extended candidate region strategy with the powerful feature extraction ability of the deep convolution neural network. Timeliness.

【學位授予單位】：華僑大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：X932;TP391.41
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本文編號：1805812