本文選題：TFT-LCD + 陷波濾波��； 參考：《湖南理工學(xué)院》2017年碩士論文

【摘要】：液晶屏的制造過(guò)程極其復(fù)雜,需要近百道工序,在制造的過(guò)程中難免會(huì)出現(xiàn)許多類型的缺陷。因此對(duì)液晶屏缺陷的檢測(cè)在生產(chǎn)過(guò)程起著關(guān)鍵性作用。利用機(jī)器視覺(jué)技術(shù)實(shí)現(xiàn)液晶屏點(diǎn)燈缺陷的高速自動(dòng)檢測(cè)是液晶屏自動(dòng)化生產(chǎn)的重要研究課題之一。論文通過(guò)分析液晶屏點(diǎn)燈圖像中存在的缺陷和瑕疵的特征,提出基于圖像處理的快速、有效的缺陷檢測(cè)方法。論文主要進(jìn)行了以下幾方面的研究:子像素級(jí)缺陷檢測(cè)方法研究:傳統(tǒng)的圖像學(xué)處理方法很難檢測(cè)出子像素級(jí)缺陷。我們通過(guò)研究液晶屏圖像的特點(diǎn),發(fā)現(xiàn)同一型號(hào)液晶屏中像素均是規(guī)則排列,因此對(duì)缺陷圖像經(jīng)過(guò)傅里葉變換能得到一致的頻譜圖,針對(duì)此特點(diǎn),論文提出了一種基于陷波濾波和圖像配準(zhǔn)的子像素缺陷檢測(cè)方法。首先用無(wú)缺陷模板圖像建立配準(zhǔn)模板和陷波濾波模板;然后用配準(zhǔn)模板對(duì)缺陷圖像進(jìn)行圖像配準(zhǔn),解決屏幕偏移問(wèn)題;再用陷波濾波模板進(jìn)行濾波處理,濾除背景紋理,使缺陷更加明顯;最后對(duì)圖像閾值分割,找出缺陷。結(jié)果顯示,該方法可以準(zhǔn)確、快速的檢測(cè)子像素級(jí)缺陷。中小型缺陷檢測(cè)方法研究:由于相機(jī)拍攝的液晶屏圖像缺陷、背景、細(xì)節(jié)、噪聲等信息都包含在一個(gè)較窄的灰度范圍內(nèi),導(dǎo)致缺陷與其他信息難以區(qū)分,因此,論文提出了一種融合局部熵與局部均勻度液晶屏缺陷檢測(cè)方法。將像素分布的局部熵值和局部均勻度值相結(jié)合,濾除空間鄰域內(nèi)噪聲并獲得像素的空間分布,利用空間特征實(shí)現(xiàn)對(duì)中小型缺陷的檢測(cè)。結(jié)果顯示,論文方法不需要進(jìn)行頻域處理便可準(zhǔn)確找到缺陷,檢測(cè)速率較高,相比于其他算法,有較強(qiáng)魯棒性。Mura缺陷檢測(cè)方法研究:液晶屏Mura缺陷均具有背景整體亮度不均、灰度變化不明顯等特點(diǎn),用基于機(jī)器視覺(jué)的方法從中檢測(cè)出缺陷是非常困難的。論文提出一種新的Mura缺陷檢測(cè)方法,首先對(duì)液晶屏圖像中背景與Mura缺陷灰度值差異進(jìn)行分析,然后采用均值濾波和背景差分法來(lái)抑制背景雜波,再利用灰度約束獲取疑似Mura缺陷區(qū)域,最后提取疑似區(qū)域的灰度特征放入訓(xùn)練后的BP神經(jīng)網(wǎng)絡(luò)提取缺陷目標(biāo)。結(jié)果顯示,論文方法有較好的除噪效果,檢測(cè)率較高。
[Abstract]:The manufacturing process of LCD screen is extremely complex and requires nearly 100 processes. Many kinds of defects will inevitably appear in the manufacturing process. Therefore, the detection of LCD screen defects plays a key role in the production process. It is one of the important research topics in the automatic production of LCD screen to realize high speed automatic detection of the defects of LCD screen with machine vision technology. Based on the analysis of defects and defects in LCD screen lamp images, a fast and effective defect detection method based on image processing is proposed in this paper. This paper mainly studies the following aspects: subpixel level defect detection method: traditional image processing methods are difficult to detect sub-pixel level defects. By studying the characteristics of the LCD screen image, we find that the pixels in the same LCD screen are arranged regularly, so we can get the consistent spectrum of the defective image by Fourier transform. In this paper, a subpixel defect detection method based on notch filtering and image registration is proposed. First, the registration template and notch filter template are established by using the non-defect template image; then, the defect image is registered with the registration template to solve the screen offset problem; and then the notch filter template is used to filter the background texture. Make the defect more obvious; finally, the image threshold segmentation, find out the defect. The results show that the method can detect subpixel defects accurately and quickly. Research on small and medium defect Detection methods: because the defects, background, details, noise and other information of LCD screen images taken by camera are all contained in a narrow gray range, it is difficult to distinguish the defects from other information. In this paper, a method for defect detection of liquid crystal screen with fusion of local entropy and local uniformity is proposed. The local entropy value and local uniformity value of pixel distribution are combined to filter the noise in the spatial neighborhood and to obtain the spatial distribution of the pixels. The detection of small and medium-sized defects is realized by using spatial features. The results show that the method can accurately find the defects without frequency domain processing, and the detection rate is higher. Compared with other algorithms, the method of robust. Mura defect detection method: the Mura defects of LCD screen have the uneven brightness of the background as a whole. It is very difficult to detect defects by machine vision based method because the gray level change is not obvious. In this paper, a new Mura defect detection method is proposed. Firstly, the difference between background and Mura defect gray value in LCD screen image is analyzed, and then the mean filter and background difference method are used to suppress background clutter. Then the suspected Mura defect region is obtained by using gray constraints, and the gray feature of the suspected area is finally extracted into the trained BP neural network to extract the defect target. The results show that the method has better denoising effect and high detection rate.
【學(xué)位授予單位】：湖南理工學(xué)院
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN873.93;TP391.41

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本文編號(hào)：1921133