本文選題：皮革　切入點：瑕疵檢測　出處：《重慶理工大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著生活水平的提高,私家車保有量激增,消費者在關(guān)注性能同時也開始注重內(nèi)部飾品質(zhì)量。皮革作為其內(nèi)部座椅等主要器件的重要材料,其品質(zhì)被嚴(yán)格要求,但由于牛皮等皮革原材料在生長生產(chǎn)過程中的蚊蟲叮咬及人為誤傷,使其表面不可避免的存在各種瑕疵,因此需要定位其表面瑕疵部分,以便控制產(chǎn)品品質(zhì)和指導(dǎo)后續(xù)生產(chǎn)加工。目前汽車座椅生產(chǎn)廠家采用的人工皮革瑕疵查找方式存在誤檢率高,效率低等缺點,而基于計算機視覺方式檢測的可行性及優(yōu)勢使得其希望能引入計算機視覺方式,替代人工,使檢測更安全,更效率,更穩(wěn)定,更客觀同時也更節(jié)約成本。針對目前皮革瑕疵檢測中瑕疵與非瑕疵區(qū)域之間的低對比度和復(fù)雜隨機紋理的干擾等原因造成其檢測難度大、速度慢;且沒有瑕疵檢測效果的客觀量化評判方法等問題,通過初步分析皮革瑕疵樣本,最終對人工視覺查找有難度的皮革微小瑕疵的自動檢測作如下幾方面工作:首先,建立皮革瑕疵檢測算法的評價方法。將瑕疵檢測看作特殊的分類工作,參考文本分類的評價指標(biāo),提出一種基于召回率和準(zhǔn)確率的評價體系。通過一種人工畫筆標(biāo)記樣本圖像中瑕疵區(qū)域并經(jīng)過識別、分割處理的方式獲取黃金標(biāo)準(zhǔn),并將其作為算法檢測結(jié)果的參考。然后通過計算定義的P、R、f及綜合評價參數(shù)F等評價指標(biāo),實現(xiàn)對瑕疵檢測結(jié)果像素級的數(shù)字化評價,為算法研究提供客觀的指導(dǎo)。其次,基于皮革瑕疵查找是人眼注意選擇機制的一種表現(xiàn),提出基于視覺顯著度的瑕疵檢測模型。基于該模型的瑕疵檢測算法,首先提取顏色、亮度特征,利用圖像本身作為模板進行對比計算顯著度圖;然后根據(jù)隨機均勻分布紋理圖像中“突出”部分顯著度高的特征,通過最顯著像素點利用區(qū)域增長法分割定位瑕疵區(qū)域。分別利用該算法與閾值法、基于模糊聚類及支持向量機等現(xiàn)有瑕疵檢測算法對皮革瑕疵樣本對比實驗,結(jié)果表明該算法解決了模板和有效特征提取困難的問題,能有效檢測微小皮革瑕疵。最后,對紋理分析技術(shù)進行研究,設(shè)計了一種基于灰度共生矩陣的紋理表述,結(jié)合基于視覺顯著度的皮革瑕疵檢測算法以進一步提高檢測效果。首先對圖像進行灰度共生矩陣的統(tǒng)計,然后計算每個像素的灰度分布頻率作為其紋理表示。該紋理特征較傳統(tǒng)基于共生矩陣的能量等紋理特征計算量大、速度慢等劣勢更適用于應(yīng)用在有一定實時性要求的皮革瑕疵檢測中,實驗結(jié)果也表明結(jié)合紋理分析后在檢測效果也有一定提高,具有一定意義。
[Abstract]:With the improvement of living standards and the increase of private car ownership, consumers are paying more attention to the quality of interior ornaments. Leather, as an important material for its internal seats and other main devices, has been strictly required for its quality. However, due to mosquito bites and manmade injuries in the course of growth and production of leather raw materials such as cowhide, there are inevitably various defects on its surface, so it is necessary to locate the defective parts of the surface. In order to control the product quality and guide the subsequent production and processing. At present, the artificial leather flaw detection method adopted by the automobile seat manufacturers has the disadvantages of high false detection rate and low efficiency. Based on the feasibility and advantages of computer vision detection, it hopes to introduce computer vision instead of manual, and make the detection safer, more efficient and more stable. It is more objective and cost saving. It is difficult and slow to detect because of the low contrast and the interference of complex random texture between the defect and the non-defect area in the current leather flaw detection. And there is no objective quantitative evaluation method of defect detection effect. Through the preliminary analysis of leather defect samples, the automatic detection of the difficult small leather defects in artificial vision is done as follows: first of all, The evaluation method of leather defect detection algorithm is established. The defect detection is regarded as a special classification work, and the evaluation index of text classification is referred to. This paper presents an evaluation system based on recall and accuracy. It is used as the reference of the algorithm detection result. Then, the digital evaluation of the pixel level of the defect detection result is realized by calculating the defined PGR f and the comprehensive evaluation parameter F, which provides objective guidance for the research of the algorithm. This paper presents a defect detection model based on visual saliency, based on which the color and luminance features are first extracted. Using the image itself as a template to compare and calculate the saliency map, and then according to the feature of high salience in the "prominent" part of the randomly distributed texture image, The most significant pixel points are segmented by using region growth method to locate the defect region. Using this algorithm and the threshold method respectively based on the existing defect detection algorithms such as fuzzy clustering and support vector machine the contrast experiment of leather defect samples is carried out. The results show that the algorithm solves the difficult problem of template and effective feature extraction, and can effectively detect small leather defects. Finally, a texture representation based on gray level co-occurrence matrix is designed, which is based on the research of texture analysis technology. Combined with the leather defect detection algorithm based on visual saliency to further improve the detection effect. Firstly, the gray level co-occurrence matrix of the image is counted. Then the gray distribution frequency of each pixel is calculated as its texture representation, which is more expensive than the traditional energy and other texture features based on co-occurrence matrix. The disadvantages such as slow speed and so on are more suitable for the leather defect detection with certain real-time requirements. The experimental results also show that the detection effect is also improved with texture analysis, which has a certain significance.
【學(xué)位授予單位】：重慶理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：U466;TP391.41

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