基于機器視覺的機床火災自動報警技術研究
發(fā)布時間：2018-08-24 13:37
【摘要】：隨著國家經濟的快速發(fā)展,時常發(fā)生的火災給工業(yè)生產帶來了巨大的損失。傳統(tǒng)的火災探測傳感器雖然對火災的預防具有重要的意義,但它們有很多的局限性,尤其在復雜的工廠生產環(huán)境中。為了解決傳統(tǒng)的檢測傳感器在一些環(huán)境的使用缺陷,國內外很多學者對視頻火災檢測算法進行了研究。本文針對火災檢測技術的研究進行了如下工作:(1)首先對火災檢測技術的發(fā)展現(xiàn)狀進行綜述,分析了傳統(tǒng)的火災探測技術的缺陷,然后分析了國內外機器視覺火災探測發(fā)展現(xiàn)狀,介紹了各個學者提出的視頻圖像檢測算法,最后得出這些技術只能在某些特殊的場合應用。(2)針對顏色檢測算法、移動物體檢測算法和紅外光譜火焰檢測算法進行研究。其中顏色檢測算法是基于統(tǒng)計學原理的方法;移動物體檢測主要包括差分法、光流法、高斯背景減除算法;本文對其進行了深入的推導。(3)針對火焰的特征分類算法進行研究,在總結這些算法的基礎上,我們提出了一種火災檢測方法,該方法結合背景減除算法和區(qū)域協(xié)方差算子,首先用顏色分布模型和自適應的背景減除算法對視頻圖像進行預處理,然后提取時空協(xié)方差矩陣。最后用支持向量機對視頻數(shù)據(jù)進行分類,得出火焰區(qū)域。并和已有文獻中的算法效果進行了對比分析。(4)針對機床現(xiàn)在應用要求,開發(fā)了一套機器視覺檢測系統(tǒng),詳細介紹了機器視覺的機床火災控制報警系統(tǒng)的系統(tǒng)結構、軟件系統(tǒng)、硬件系統(tǒng)。該軟件系統(tǒng)已經在北京電加工研究所現(xiàn)場實際運行。經測試,該軟件系統(tǒng)算法可靠性強、探測率高。能夠滿足電加工機床現(xiàn)場需要。本文提出了一種火災檢測方法,該方法結合背景減除算法和區(qū)域協(xié)方差算子。最后用支持向量機對視頻數(shù)據(jù)進行分類,得出火焰區(qū)域。針對北京電加工研究所的特殊應用,開發(fā)了一套針對機床火災的機器視覺系統(tǒng)。
[Abstract]:With the rapid development of national economy, frequent fires have brought huge losses to industrial production. Although the traditional fire detection sensors are of great significance to fire prevention, they have many limitations, especially in the complex production environment of factories. In order to solve the defects of traditional detection sensors in some environments, many scholars at home and abroad have studied the video fire detection algorithm. In this paper, the research work of fire detection technology is as follows: (1) the development of fire detection technology is summarized, the defects of traditional fire detection technology are analyzed, and the present situation of fire detection by machine vision at home and abroad is analyzed. This paper introduces the video image detection algorithms proposed by various scholars, and concludes that these techniques can only be applied in some special situations. (2) the color detection algorithm, moving object detection algorithm and infrared spectrum flame detection algorithm are studied. Color detection algorithm is based on the principle of statistics; moving object detection mainly includes difference method, optical flow method, Gao Si background subtraction algorithm. On the basis of summarizing these algorithms, we propose a fire detection method, which combines background subtraction algorithm and regional covariance operator. Firstly, the color distribution model and adaptive background subtraction algorithm are used to preprocess the video image. Then the space-time covariance matrix is extracted. Finally, the support vector machine is used to classify the video data and the flame region is obtained. And compared with the existing literature algorithm results. (4) according to the current application requirements of machine tools, a set of machine vision detection system is developed. The system structure and software system of machine tool fire control and alarm system based on machine vision are introduced in detail. Hardware system. The software system has been in practical operation in Beijing Institute of Electrical processing. After testing, the software system has strong reliability and high detectability. Able to meet the field needs of electrical machining machine tools. In this paper, a fire detection method is proposed, which combines background subtraction algorithm and regional covariance operator. Finally, the support vector machine is used to classify the video data and the flame region is obtained. Aiming at the special application of Beijing Institute of Electrical processing, a machine vision system for machine tool fire is developed.
【學位授予單位】：沈陽理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TG502.39;TP391.41
本文編號：2200980
[Abstract]:With the rapid development of national economy, frequent fires have brought huge losses to industrial production. Although the traditional fire detection sensors are of great significance to fire prevention, they have many limitations, especially in the complex production environment of factories. In order to solve the defects of traditional detection sensors in some environments, many scholars at home and abroad have studied the video fire detection algorithm. In this paper, the research work of fire detection technology is as follows: (1) the development of fire detection technology is summarized, the defects of traditional fire detection technology are analyzed, and the present situation of fire detection by machine vision at home and abroad is analyzed. This paper introduces the video image detection algorithms proposed by various scholars, and concludes that these techniques can only be applied in some special situations. (2) the color detection algorithm, moving object detection algorithm and infrared spectrum flame detection algorithm are studied. Color detection algorithm is based on the principle of statistics; moving object detection mainly includes difference method, optical flow method, Gao Si background subtraction algorithm. On the basis of summarizing these algorithms, we propose a fire detection method, which combines background subtraction algorithm and regional covariance operator. Firstly, the color distribution model and adaptive background subtraction algorithm are used to preprocess the video image. Then the space-time covariance matrix is extracted. Finally, the support vector machine is used to classify the video data and the flame region is obtained. And compared with the existing literature algorithm results. (4) according to the current application requirements of machine tools, a set of machine vision detection system is developed. The system structure and software system of machine tool fire control and alarm system based on machine vision are introduced in detail. Hardware system. The software system has been in practical operation in Beijing Institute of Electrical processing. After testing, the software system has strong reliability and high detectability. Able to meet the field needs of electrical machining machine tools. In this paper, a fire detection method is proposed, which combines background subtraction algorithm and regional covariance operator. Finally, the support vector machine is used to classify the video data and the flame region is obtained. Aiming at the special application of Beijing Institute of Electrical processing, a machine vision system for machine tool fire is developed.
【學位授予單位】：沈陽理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TG502.39;TP391.41
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