本文關(guān)鍵詞： 瑕疵檢測 圖像分塊二值化 中值濾波 特征提取 基元　出處：《吉林大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：花邊布又稱抽紗、蕾絲,是指有花紋圖案的、用于裝飾的帶狀織物.我國生產(chǎn)花邊的歷史雖然晚于歐洲,但生產(chǎn)技術(shù)水平提高速度很快,已形成了較大的生產(chǎn)規(guī)模.在紡織品的生產(chǎn)過程中,瑕疵檢測對于提升織物的品質(zhì)起著十分重要的作用.目前我國的紡織品主要依賴于人工對其進(jìn)行質(zhì)檢,檢測過程容易受到人為因素影響,誤檢情況時有發(fā)生.為降低生產(chǎn)成本、提高檢測準(zhǔn)確率,紡織品瑕疵自動檢測應(yīng)逐步替代人工檢測.本文的目的是設(shè)計有實際應(yīng)用價值的花邊布自動瑕疵檢測算法.以橫W紋花邊布為主要研究對象,瑕疵檢測算法分為三個步驟:圖像預(yù)處理、布面的特征提取、瑕疵檢測.圖像預(yù)處理主要包括圖像二值化和圖像去噪兩部分.由于織物生產(chǎn)環(huán)境復(fù)雜,拍攝的花邊布圖像存在著亮度不均的問題,這給下一步的特征提取帶來了很大困難.為解決這個問題,本文對灰度圖像二值化的方法進(jìn)行研究,提出了一種基于大津法的圖像分塊二值化方法,先在矩形網(wǎng)格下利用傳統(tǒng)的大津法求小塊矩形圖像的閾值,再在三角形網(wǎng)格下,將閾值做平滑處理,最終得到更為理想的二值化圖像.然后利用中值濾波,去除二值圖像中對圖案紋理產(chǎn)生影響的噪聲,為下一步的布面特征提取做好準(zhǔn)備工作.在布匹中,其圖案紋理是有周期性的,存在一個尺寸最小的區(qū)域,整個布匹的圖案紋理可以通過這個區(qū)域做平移操作得到,這個最小區(qū)域稱為格子.格子可以被分解為更精密的組成成分,稱為基元,通過基元的平移、旋轉(zhuǎn)、鏡面反射等操作可以得到格子.在布面的特征提取部分,借鑒人臉識別特征提取方法中的基于局部特征的方法,基本思想是利用圖案紋理的局部幾何特性,找出每個基元的關(guān)鍵點;然后利用關(guān)鍵點對花邊布進(jìn)行分塊處理,得到花邊布的格子和基元圖像.第三步為瑕疵檢測,利用Ngan等人提出的運動差能量和方差的方法.這種方法可以忽略基元圖像的輕微變形和不對齊,同時運動差能量放大了有瑕疵基元的瑕疵信息,使得瑕疵更容易被檢測出.通過確定無瑕疵格子中任意兩個基元的運動差能量和方差的取值范圍,判斷一幅格子圖像中是否存在瑕疵.若能量和方差的計算結(jié)果在確定的范圍內(nèi),則圖像無瑕疵,反之則有瑕疵.這種方法可以判斷出較大面積的花紋錯亂、油污等瑕疵.為了可以進(jìn)一步判斷出較小面積的瑕疵如孔洞、劃痕等,本文利用上述的方法,對從基元中分割出的子窗口圖像繼續(xù)進(jìn)行瑕疵檢測.對現(xiàn)有的217張圖像進(jìn)行了實驗,結(jié)果表明本文提出的花邊布瑕疵檢測算法具有可行性.
[Abstract]:Lace fabric, also called lace, refers to the ribbon fabric with pattern pattern, which is used for decoration. Although the history of lace production in China is later than that in Europe, the level of production technology improves rapidly. In the process of textile production, defect detection plays a very important role in improving the quality of fabrics. At present, Chinese textiles mainly rely on manual quality inspection. The detection process is easy to be affected by human factors and false detection occurs from time to time. In order to reduce production costs and improve the accuracy of detection. Automatic detection of textile defects should be replaced by manual detection step by step. The purpose of this paper is to design an automatic flaw detection algorithm for lace cloth with practical application value. Defect detection algorithm is divided into three steps: image preprocessing, fabric feature extraction, defect detection. Image preprocessing mainly includes image binarization and image denoising. In order to solve this problem, the method of binarization of gray image is studied in this paper. In this paper, a method of image segmentation based on Otsu method is proposed. Firstly, the threshold value of small rectangular image is obtained by using the traditional Otsu method under rectangular mesh, and then the threshold value is smoothed under triangular mesh. Finally, a more ideal binary image is obtained, and then the median filter is used to remove the noise that affects the pattern texture in the binary image, so as to prepare for the next step of fabric feature extraction. Its pattern texture is periodic, there is a minimum size of the region, the entire fabric pattern texture can be obtained by this region for translation operation. This minimum region is called a lattice. The lattice can be decomposed into more precise components called primitives. The feature extraction part of the fabric can be obtained by the translation rotation and mirror reflection of the elements. The basic idea is to find out the key points of each element by using the local geometric characteristics of pattern texture for reference to the method of feature extraction based on local features of face recognition. Then we use the key points to divide the lace cloth into blocks to get the lattice and basic image of the lace cloth. The third step is defect detection. Using the method of motion difference energy and variance proposed by Ngan et al. This method can ignore the slight distortion and misalignment of the primitive image and amplify the defective information of the defective primitive by moving difference energy. By determining the range of the difference energy and variance of the motion of any two elements in the blemeless lattice, the defect can be detected more easily. If the calculation results of energy and variance are within a certain range, the image has no defects, otherwise there are defects. This method can determine the large area of pattern disorder. In order to further judge the smaller areas of defects such as holes, scratches and so on, this paper uses the above method. The defect detection of the sub-window image from the primitive is carried out. 217 images are tested, and the results show that the algorithm proposed in this paper is feasible.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TS101.97;TP391.41

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