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基于多元圖像分析的包裝罐內(nèi)壁缺陷檢
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Cans Inner Surface Inspection System Based on Multivariate Image Analysis
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    摘要:

    為提高包裝罐生產(chǎn)線內(nèi)壁缺陷檢測準(zhǔn)確性與可靠性,研究了一種采用單攝像機的內(nèi)壁缺陷檢測系統(tǒng),。利用基于形態(tài)學(xué)的區(qū)域提取算法,,從罐內(nèi)圖像中分割出內(nèi)壁檢測區(qū)域圖像。提出基于多元圖像分析(MIA)的內(nèi)壁缺陷檢測算法,。利用圖像融合構(gòu)成環(huán)形合格樣本圖像,,消除罐內(nèi)焊縫區(qū)域的影響,把多個環(huán)形合格樣本圖像與測試樣本內(nèi)壁檢測區(qū)域圖像堆疊起來,,用重合區(qū)域的圖像構(gòu)造多元測試圖像,。用基于主成分分析(PCA)的多元圖像處理方法獲得多元測試圖像的主分量表示,,將去掉第一主分量和噪聲后的Q統(tǒng)計圖像作為內(nèi)壁缺陷特征的檢測空間,利用閾值處理檢測缺陷,,解決了罐體內(nèi)壁照明困難,、亮度不均造成缺陷誤檢率高的問題,提高了檢測系統(tǒng)的準(zhǔn)確性和魯棒性,。實驗表明對內(nèi)壁缺陷檢測的誤檢率降低到2%,,驗證了檢測系統(tǒng)的有效性和可靠性。

    Abstract:

    In order to improve the accuracy and reliability of defect detection for packaging cans production lines, an inner surface inspection system with a single camera was studied. By using morphological region extraction algorithms the inspection region of interest image can be obtained from the whole inner image. For defect feature detection an approach based on multivariate image analysis (MIA) was proposed. To cancel the effect of seam regions in the inner images, a method of images fusion was implemented to form the ring-like good sample image without seam region. By stacking both the ring-like good sample images and the test image, the multivariate test images were constructed with their overlapping part. By using MIA technique with principal component analysis (PCA), the principal component scores of the multivariate test images were obtained. As the feature space for defect detection the Q statistic image was derived from the residuals which were left after the extraction of the first PC and noise. The surface defects can be effectively detected using an appropriate threshold. The experimental results show that the proposed inspection system has less sensitivity to the inhomogeneous of illumination, and has more robustness and reliability with pseudo reject rate reducing to 2%.

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王宣銀,梁冬泰.基于多元圖像分析的包裝罐內(nèi)壁缺陷檢[J].農(nóng)業(yè)機械學(xué)報,2009,40(6):222-226. Cans Inner Surface Inspection System Based on Multivariate Image Analysis[J]. Transactions of the Chinese Society for Agricultural Machinery,2009,40(6):222-226.

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