本文關(guān)鍵詞：基于機器視覺的端塞表面缺陷檢測算法研究及實現(xiàn)　出處：《陜西科技大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 機器視覺 表面缺陷檢測 圖像背景補償 極坐標(biāo)變換 Open CV

【摘要】：隨著人類對新能源的需求量不斷增加,核能作為一種新型能源,以其較高的能源集度和豐富的能源儲存量越來越多地受到了人們的關(guān)注。其中核電是核能使用的一種重要形式,核電具有環(huán)保、經(jīng)濟性高等優(yōu)點,但是核電的使用在安全上有著較高的要求。核電中核反應(yīng)堆的安全最為重要,而燃料棒是核反應(yīng)堆中重要的組成部分,燃料棒端塞(以下簡稱端塞)作為燃料棒的一種核心零件,起著防止反應(yīng)物溢出的作用,其表面的缺陷直接影響到核反應(yīng)堆的安全。目前針對端塞表面缺陷的檢測基本還停留在人工檢測階段,人工檢測存在勞動強度大,檢測效率低等問題。針對這些問題,利用機器視覺技術(shù)和圖像處理算法完成對產(chǎn)品缺陷檢測成為了一種更好的方法。本文針對端塞表面的兩大類缺陷——側(cè)表面缺陷和中心孔缺陷,在研究缺陷特征的基礎(chǔ)上,分別設(shè)計了兩種表面缺陷檢測算法,并進(jìn)行了算法的仿真研究;同時設(shè)計了端塞缺陷檢測系統(tǒng)的硬件測試平臺,并且開發(fā)了相應(yīng)的檢測軟件。本文的主要工作包括:(1)端塞表面缺陷檢測硬件測試平臺的設(shè)計本文設(shè)計并實現(xiàn)了“旋轉(zhuǎn)平臺+LED前向光源+線陣相機”和“LED背光源+面陣相機”的硬件測試平臺設(shè)計方案,分別對端塞側(cè)表面圖像和中心孔圖像進(jìn)行采集,并且利用計算機完成對采集圖像的處理。(2)端塞表面缺陷檢測算法的研究和設(shè)計本文通過對端塞側(cè)表面和中心孔缺陷特征的分析,采用了“圖像預(yù)處理+缺陷提取+缺陷分類”的檢測流程。并在兩個流程中,根據(jù)端塞側(cè)表面缺陷圖像和中心孔缺陷圖像的特性,設(shè)計了相應(yīng)的檢測算法。(1)端塞側(cè)表面缺陷檢測算法端塞側(cè)表面圖像是在前向光源照射下由線陣相機拍攝的圖像,本文根據(jù)端塞側(cè)表面缺陷圖像的特征,利用中值濾波和基于多項式的背景補償方法對側(cè)表面缺陷圖像進(jìn)行預(yù)處理;采用閾值分割提取缺陷,并通過形態(tài)學(xué)分析的方法去除“非缺陷點”,提高缺陷檢測準(zhǔn)確性;最后通過支持向量機分類完成對側(cè)表面缺陷的分類。(2)端塞中心孔缺陷檢測算法端塞中心孔圖像是在背光源照射下由面陣相機拍攝的圖像,本文通過對中心孔邊緣信息的極坐標(biāo)空間處理來檢測中心孔缺陷。具體過程:首先利用基于灰度梯度的濾波方法對中心孔缺陷圖像進(jìn)行預(yù)處理;其次采用改進(jìn)的雙峰法進(jìn)行閾值分割,并通過邊緣檢測提取圓邊緣信息,再利用極坐標(biāo)變換獲得缺陷信息;最后通過二叉樹分類器完成對中心孔缺陷的分類。(3)端塞表面缺陷檢測軟件的設(shè)計和實現(xiàn)在VS2010軟件開發(fā)環(huán)境下,以C++語言為基礎(chǔ),利用MFC框架、多線程技術(shù)、Open CV視覺開發(fā)庫等編程技術(shù)設(shè)計開發(fā)端塞表面缺陷檢測軟件。該軟件通過對界面的設(shè)計、缺陷檢測算法的移植、和數(shù)據(jù)庫的調(diào)用,實現(xiàn)了端塞表面缺陷的檢測、檢測過程的監(jiān)控和缺陷信息的存儲等功能。本文通過對端塞表面缺陷檢測硬件平臺的設(shè)計,算法的研究以及相應(yīng)的軟件實現(xiàn),得到了一種基于機器視覺的端塞表面缺陷檢測算法及軟件實現(xiàn)。實驗結(jié)果表明,本文所設(shè)計的端塞表面缺陷檢測算法和軟件,對側(cè)表面缺陷的檢測正確率可達(dá)到86%以上,對中心孔缺陷的檢測正確率可以達(dá)到90%,基本可以達(dá)到端塞表面缺陷檢測的要求。
[Abstract]:With the demand for new energy increasing, nuclear power as a new energy for its high energy intensity and abundant energy storage capacity by more and more people's attention. The nuclear power is an important form of the use of nuclear power, nuclear power has the advantages of environmental protection, economic advantages, but nuclear power use a higher requirements on safety of nuclear power. Nuclear reactor safety is most important, which is an important part of the fuel rods in nuclear reactors, fuel rods (hereinafter referred to as the end plug) as a core part of fuel rods, can prevent the overflow of reactants, the surface defects directly affects the safety of nuclear reactors. For the detection of surface defects of the end plug remains in the manual detection stage, artificial detection in the presence of high labor intensity, low efficiency of detection problems. To solve these problems, the use of machine vision McGregor technology and image processing algorithm of product defect detection has become a better method. According to the two kinds of defects in the end plug surface - hole side surface defects and defect center, based on defect characteristics, respectively the two kinds of surface defects detection algorithm design, and simulation algorithm; at the same time the design of hardware testing platform end plug defect detection system, and developed the corresponding software. The main work of this paper includes: (1) design the design end plug surface defect detection hardware test platform and implement the "test platform design scheme of +LED rotating platform to source + line camera" and "LED back light + camera" hardware, were collected on the end plug side surface image and center hole image, and use the computer to complete the processing of image acquisition. (2) the end plug surface defect detection algorithm The research and design based on the analysis of the end plug side surface and center hole defect characteristics, the detection process of image pretreatment + defect extraction + defect classification. And in the two process, according to the characteristics of the end plug side surface defect image and center hole defect image, the corresponding detection algorithm design. (1) surface defect detection algorithm end plug side end plug side surface image is in the forward light source by linear array camera, according to the characteristics of the end plug side surface defect image, and background compensation method based on polynomial on the side surface defect image is preprocessed by using median filter; threshold segmentation the extraction method of defects, and through morphological analysis to remove "non defect", to improve the accuracy of defect detection; finally, the support vector machine classification complete classification of side surface defects. (2) the end plug hole defect detection center The end plug hole in the center of the image algorithm is image captured by the camera in the back light source, the detection center hole by polar coordinate space processing of center hole edge information. The specific process: first using the filtering method based on gray gradient of center hole defect image preprocessing; secondly the improved Shuangfeng method threshold segmentation, edge detection and edge information through extraction, then the defect information using polar coordinate transform; finally completes the classification of center hole defects by two binary tree classifier. (3) the design of end plug surface defect detection software and the realization of VS2010 in the software development environment, based on the C++ language, using MFC the framework, multi threading technology, design and development of Open CV visual programming technology development base end plug surface defect detection software. The software through the design of the interface, defect detection algorithm and the number of transplantation. According to the data base, realize the detection of the end plug surface defect detection process, monitoring and defect information storage and other functions. In this paper, through the design of the terminal hardware platform plug surface defect detection algorithm, and the corresponding software, get a machine vision end plug surface defect detection algorithm and software implementation based on the experimental results show that the end plug surface defect detection algorithm and the software designed in this paper, detection of the side surface defect accuracy can reach more than 86%, detection of the central hole in the correct rate can reach 90%, can reach the end surface defect inspection plug.

【學(xué)位授予單位】：陜西科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.41;TM623

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 馬玉真,胡亮,方志強,曹素芝;計算機視覺檢測技術(shù)的發(fā)展及應(yīng)用研究[J];濟南大學(xué)學(xué)報(自然科學(xué)版);2004年03期

2 徐國旺,廖明潮;擬合圓的幾種方法[J];武漢工業(yè)學(xué)院學(xué)報;2002年04期

3 侯宇;基于Hough變換的圖象檢測對偶點法[J];中國圖象圖形學(xué)報;2001年08期

4 薛祥義,白新德,田振業(yè),文惠民,李曉寧;核燃料棒端塞用冷軋鋯合金棒材橫截面低倍組織異�，F(xiàn)象的研究[J];稀有金屬材料與工程;2004年08期

5 孔兵,王昭,譚玉山;基于圓擬合的激光光斑中心檢測算法[J];紅外與激光工程;2002年03期

6 劉元朋,張定華,桂元坤,李永奇;用帶約束的最小二乘法擬合平面圓曲線[J];計算機輔助設(shè)計與圖形學(xué)學(xué)報;2004年10期

7 梁華為;;直接從雙峰直方圖確定二值化閾值[J];模式識別與人工智能;2002年02期

8 段瑞玲,李慶祥,李玉和;圖像邊緣檢測方法研究綜述[J];光學(xué)技術(shù);2005年03期

9 曹文倫;史忠科;封建湖;;分形維數(shù)及其在圖像分類中的應(yīng)用研究[J];計算機應(yīng)用研究;2007年04期

10 虞旦;韋巍;;改進(jìn)的隨機圓檢測算法[J];中國圖象圖形學(xué)報;2009年08期

相關(guān)博士學(xué)位論文 前1條

1 呂哲;注塑制品視覺檢測關(guān)鍵技術(shù)研究[D];東北大學(xué);2009年

相關(guān)碩士學(xué)位論文 前4條

1 張慧;基于SVD和SVM的復(fù)雜背景噪聲圖像的紙病辨識[D];陜西科技大學(xué);2015年

2 聶振宇;金屬部件表面缺陷視覺檢測系統(tǒng)研究[D];中南大學(xué);2013年

3 董保全;基于機器視覺的鋼板表面缺陷檢測系統(tǒng)的關(guān)鍵技術(shù)研究[D];浙江理工大學(xué);2013年

4 饒剛;支持向量機（SVM）算法的進(jìn)一步研究[D];重慶大學(xué);2012年

，

本文編號：1391159