本文選題：印刷電路板　切入點(diǎn)：缺陷檢測(cè)　出處：《重慶理工大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：當(dāng)今,電子工業(yè)在國(guó)家經(jīng)濟(jì)發(fā)展中扮演著越來(lái)越重要的角色,作為各種電子元器件的高度信息集合,印刷電路板(PCB)被廣泛應(yīng)用在電子工業(yè)中的各個(gè)領(lǐng)域。經(jīng)濟(jì)的不斷發(fā)展促使電子技術(shù)不斷地提高,輕薄、便捷的電子工藝成為潮流,高密集、高集成度成為PCB的發(fā)展趨勢(shì),這給傳統(tǒng)的PCB質(zhì)檢帶來(lái)了十分巨大的挑戰(zhàn)。傳統(tǒng)的人工檢測(cè)方法存在著速度慢、時(shí)間長(zhǎng)和易漏檢等等問(wèn)題,完全無(wú)法適應(yīng)技術(shù)和工藝的快速發(fā)展,怎樣實(shí)現(xiàn)精準(zhǔn)、高效的PCB自動(dòng)缺陷檢測(cè),一直是電子工業(yè)領(lǐng)域非常重視的一個(gè)問(wèn)題。同時(shí),個(gè)人和中小企業(yè)對(duì)檢測(cè)PCB缺陷的需求越來(lái)越高,能否達(dá)到低成本、高精度是首要考慮的問(wèn)題。因此,研究如何通過(guò)低成本的圖像處理技術(shù)提高PCB板缺陷檢測(cè)的精度具有重要的研究意義�；趫D像的PCB缺陷檢測(cè)流程包括圖像預(yù)處理、圖像配準(zhǔn)、圖像分割和圖像識(shí)別等方面,其中圖像預(yù)處理又包含圖像增強(qiáng)、圖像平滑和圖像銳化操作。根據(jù)以上流程和檢測(cè)的需要,論文的三個(gè)主要工作及研究?jī)?nèi)容如下:1.對(duì)比分析得到適當(dāng)算法。為了在圖像配準(zhǔn)前和圖像分割后得到較為理想的目標(biāo)處理圖像,本文在現(xiàn)有算法的基礎(chǔ)上進(jìn)行算法的對(duì)比分析,最終選取灰度變換,自適應(yīng)濾波,梯度算子作為預(yù)處理流程的處理算法;選取最大類(lèi)間方差閥值作為圖像分割操作的算法。分析表明,得到的適當(dāng)算法有利于PCB圖像的配準(zhǔn)和識(shí)別的工作。2.改進(jìn)RHT提高配準(zhǔn)精度。在圖像配準(zhǔn)操作上,針對(duì)傳統(tǒng)算法計(jì)算量大,耗時(shí)較多的問(wèn)題,本文提出了一種結(jié)合主成分分析(PCA)和分段隨機(jī)霍夫變換的PCB板圓Mark定位的算法。該算法首先對(duì)載入原始彩色PCB板圖像進(jìn)行灰度二值化后采用Canny算子提取圖像邊緣,然后去掉所得到圖像中的交叉點(diǎn)和小線段,并標(biāo)記剩下的線段,找出大于所設(shè)閥值的線段,再運(yùn)用PCA分析降維去噪,并保留類(lèi)圓的曲線段,之后采用分段RHT分析得到圓的個(gè)數(shù)及相關(guān)參數(shù),最后結(jié)合以上得到的圓參數(shù),通過(guò)最小二乘擬合得到所要的圓Mark。實(shí)驗(yàn)表明:相比與傳統(tǒng)的模版匹配和隨機(jī)霍夫變換,該算法有效地提高了圓Mark的識(shí)別精度和定位精度。3.設(shè)計(jì)系統(tǒng)識(shí)別缺陷。利用MATLAB的圖像工具庫(kù)和上述算法的實(shí)現(xiàn)和改進(jìn)原理,通過(guò)MATLAB的GUI功能編制PCB缺陷檢測(cè)軟件。首先分析了PCB缺陷檢測(cè)軟件的處理流程和缺陷識(shí)別方法,然后對(duì)編制的軟件系統(tǒng)進(jìn)行了功能展示,最后通過(guò)缺陷檢測(cè)實(shí)例測(cè)試了軟件分別在檢測(cè)短路斷路和毛刺缺損的識(shí)別效果。實(shí)驗(yàn)表明:本文實(shí)現(xiàn)的軟件系統(tǒng)能精確地檢測(cè)出PCB板的短路、斷路、缺損、毛刺等缺陷。
[Abstract]:Nowadays, the electronic industry plays a more and more important role in the national economic development. Printed circuit board (PCB) has been widely used in various fields of electronic industry. With the development of economy, electronic technology has been continuously improved, thin and convenient electronic technology has become the trend, high density and high integration become the development trend of PCB. This brings a great challenge to the traditional PCB quality inspection. The traditional manual inspection methods have some problems, such as slow speed, long time and easy to miss the inspection, so they can not adapt to the rapid development of technology and technology, and how to achieve precision. Efficient PCB automatic defect detection has always been a very important problem in the electronic industry. At the same time, the demand of individuals and small and medium-sized enterprises to detect PCB defects is increasing. Whether or not to achieve low cost and high precision is the first consideration. It is of great significance to study how to improve the accuracy of PCB board defect detection by low cost image processing technology. The PCB defect detection flow based on image includes image preprocessing, image registration, image segmentation and image recognition. Image preprocessing includes image enhancement, image smoothing and image sharpening. The three main work and research contents of this paper are as follows: 1.Contrastive analysis of the appropriate algorithm. In order to get a better target processing image before image registration and after image segmentation, this paper carries out a comparative analysis of the algorithms based on the existing algorithms. Finally, grayscale transform, adaptive filter and gradient operator are selected as preprocessing algorithms, and the maximum inter-class variance threshold is chosen as the algorithm for image segmentation. The appropriate algorithm is propitious to the work of PCB image registration and recognition. The improvement of RHT improves the registration accuracy. In the image registration operation, the traditional algorithm has a lot of computation and time consuming. In this paper, an algorithm of PCB circular Mark location based on principal component analysis (PCA) and piecewise random Hough transform is proposed. Firstly, the image edge is extracted by using Canny operator after binarization of the gray scale of the loaded original color PCB board image. Then the intersection points and small line segments in the image are removed, and the remaining line segments are marked to find the lines larger than the threshold, and then to use PCA analysis to reduce the dimension and noise, and to preserve the curve segments of the circle. Then, the number of circles and the relative parameters are obtained by piecewise RHT analysis. Finally, the circle Mark is obtained by least square fitting combined with the above circle parameters. The experimental results show that compared with the traditional template matching and random Hough transform, The algorithm effectively improves the recognition accuracy and positioning accuracy of circular Mark. 3. The design system identifies defects. Using the image tool library of MATLAB and the realization and improvement principle of the above algorithm, The GUI function of MATLAB is used to compile the PCB defect detection software. Firstly, the processing flow and defect identification method of PCB defect detection software are analyzed, and then the function of the software system is demonstrated. Finally, the effect of the software in detecting short circuit break and burr defect is tested by an example of defect detection. The experiment shows that the software system can accurately detect the short circuit, open circuit, defect, burr and other defects of PCB board.
【學(xué)位授予單位】：重慶理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.41;TN41

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張波,李銳華,彭年才,史維祥;基于小波變換的PCB缺陷視覺(jué)檢測(cè)技術(shù)研究[J];系統(tǒng)仿真學(xué)報(bào);2004年08期

2 林小竹;籍俊偉;谷瑩瑩;;圖像歐拉數(shù)的研究與應(yīng)用[J];石油化工高等學(xué)校學(xué)報(bào);2007年02期

3 李平;錢(qián)琳琳;;利用單映射法改善圖像直方圖均衡化的精度[J];北京聯(lián)合大學(xué)學(xué)報(bào)(自然科學(xué)版);2006年03期

4 李志敏;林越偉;黃俊;張鳳陽(yáng);萬(wàn)睿;張晶;黃凡;;PCB走線檢測(cè)的預(yù)處理算法[J];光學(xué)精密工程;2007年02期

5 林小竹;沙蕓;籍俊偉;王彥敏;;關(guān)于二維圖像Euler數(shù)新公式的證明[J];中國(guó)科學(xué)E輯:信息科學(xué);2006年04期

6 孫增國(guó);韓崇昭;;基于Laplacian算子的圖像增強(qiáng)[J];計(jì)算機(jī)應(yīng)用研究;2007年01期

7 葉帆;周寧;;基于濾波與圖像序列灰度相關(guān)的小目標(biāo)檢測(cè)方法[J];技術(shù)與市場(chǎng);2007年04期

8 馮斌;王璋;孫俊;;基于量子粒子群算法的Ostu圖像閾值分割[J];計(jì)算機(jī)工程與設(shè)計(jì);2008年13期

9 張白雯;吳正云;;基于小波變換和神經(jīng)網(wǎng)絡(luò)的PCB檢測(cè)[J];微計(jì)算機(jī)信息;2008年27期

10 龔磊;葉玉堂;吳云峰;樊星;張傳義;;基于自動(dòng)光學(xué)檢測(cè)技術(shù)的印刷電路板檢孔裝置[J];中國(guó)科技信息;2009年07期

相關(guān)博士學(xué)位論文 前3條

1 曾磊;印刷電路板三維CT圖像處理與分析技術(shù)研究[D];解放軍信息工程大學(xué);2013年

2 胡濤;基于輪廓對(duì)比的PCB裸板缺陷檢測(cè)算法研究[D];華中科技大學(xué);2009年

3 陳世哲;微電子產(chǎn)品視覺(jué)檢測(cè)中關(guān)鍵技術(shù)研究[D];哈爾濱工業(yè)大學(xué);2006年

相關(guān)碩士學(xué)位論文 前9條

1 王雷;基于人臉檢測(cè)的疲勞駕駛分析[D];合肥工業(yè)大學(xué);2014年

2 喬寅;基于視頻的灰度圖像彩色化算法研究[D];沈陽(yáng)理工大學(xué);2014年

3 馬燦;PCB缺陷智能視覺(jué)檢測(cè)系統(tǒng)研究與設(shè)計(jì)[D];湖南大學(xué);2012年

4 齊立榮;基于圖像處理的PCB缺陷自動(dòng)光學(xué)檢測(cè)系統(tǒng)的研究與實(shí)現(xiàn)[D];北京郵電大學(xué);2010年

5 劉陽(yáng);基于圖像處理的PCB焊接缺陷檢測(cè)技術(shù)研究[D];大連理工大學(xué);2009年

6 胡文娟;基于機(jī)器視覺(jué)的PCB光板缺陷檢測(cè)技術(shù)研究[D];武漢理工大學(xué);2007年

7 謝佳豪;基于計(jì)算機(jī)視覺(jué)的印刷電路板自動(dòng)檢測(cè)系統(tǒng)的研究[D];西安理工大學(xué);2004年

8 柳祥樂(lè);基于小波的圖像壓縮和邊緣檢測(cè)研究[D];南京理工大學(xué);2004年

9 鄭偉;圖像法檢測(cè)印刷電路板缺陷[D];西安理工大學(xué);2002年

，

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