發(fā)布時間：2018-03-27 20:18

  本文選題：玻璃纖維布　切入點：機器視覺　出處：《鄭州大學》2017年碩士論文

【摘要】：隨著經(jīng)濟的發(fā)展,我國紡織業(yè)步入迅猛發(fā)展的階段。然而國內(nèi)大多數(shù)紡織品生產(chǎn)企業(yè)勞動密集程度較高,紡織品的缺陷檢測仍然依靠人工檢測,這種方式存在主觀性強,精確度低,工作強度高等諸多弊端。機器視覺技術的日益成熟,使其在工業(yè)生產(chǎn)過程中的應用越來越為廣泛,基于機器視覺技術的紡織品在線缺陷檢測已然成為紡織品質(zhì)量控制的重要發(fā)展方向。國外的織物在線檢測技術起步相對較早,然而從外國引進織物在線缺陷檢測設備價格昂貴,成本較高。國內(nèi)的研究主要是針對某一種算法的研究且僅適用于一種織物缺陷檢測,不能直接應用于玻璃纖維布的缺陷檢測生產(chǎn)實際中。因此研究玻璃纖維布缺陷檢測的關鍵技術,對于推動玻璃纖維織物自動化生產(chǎn)和布匹質(zhì)量快速分級具有極其重要的意義。本文對玻璃纖維布缺陷檢測系統(tǒng)的關鍵技術進行了深入系統(tǒng)地研究,主要內(nèi)容包括:基于玻璃纖維布的紋理特性、檢測要求和生產(chǎn)環(huán)境等設計了玻璃纖維布缺陷檢測系統(tǒng)的總體方案,搭建了玻璃纖維布機器視覺檢測平臺,根據(jù)玻璃纖維布的缺陷特征,確定了背光照明的光源配置方案和基于GigE的多相機檢測方案,獲得了高對比度織物圖像,降低了缺陷識別難度。針對被檢玻璃纖維布布幅較寬和工業(yè)CCD視場小的問題,提出采用多相機同步采集圖像然后對采集到的多幅圖像進行拼接處理的實用性方案。分別基于模板匹配拼接方法和基于Harris特征點拼接方法研究了玻璃纖維布圖像拼接技術,并從配準精度、拼接速度等方面對二者進行了對比分析。本文從實時性和可靠性出發(fā),選擇了基于模板匹配的拼接方法進行玻璃纖維布圖像的拼接工作。為了解決玻璃纖維織物在線檢測效率低、實時性差等問題,提出了一種基于Blob分析的織物缺陷檢測方法。首先對織物圖像采用均值濾波器進行平滑處理,以削弱噪聲和織物紋理的干擾,然后采用迭代法尋找最佳閾值將圖像分割為Blob和背景的像素集合,采用形態(tài)學處理調(diào)整分割后的Blob形狀,最后對圖像進行連通性分析和特征提取,通過對Blob區(qū)域進行最小外接矩形擬合得到缺陷特征的個數(shù)和尺寸等信息,實現(xiàn)了玻璃纖維布劈縫、跳花、破洞、污漬等常見缺陷的識別。實驗結果表明,該方法計算簡單,檢測結果穩(wěn)健可靠,實時性好,是一種有效的織物缺陷在線檢測方法。在VS2010平臺下基于C#、Halcon和SQL Sever數(shù)據(jù)庫研制了玻璃纖維織布缺陷檢測軟件系統(tǒng),系統(tǒng)包括圖像采集模塊、人機交互模塊、圖像處理模塊和缺陷數(shù)據(jù)統(tǒng)計模塊,實現(xiàn)了玻璃纖維布缺陷的檢測和布匹分級。在實驗平臺上進行了調(diào)試和實驗驗證,結果表明本文的研究方法穩(wěn)定可靠、實時性好,滿足了預期的研發(fā)要求。
[Abstract]:With the development of economy, China's textile industry has stepped into a stage of rapid development. However, most domestic textile production enterprises have a relatively high labor intensity, and the testing of textile defects still depends on manual detection, which has strong subjectivity. With the development of machine vision technology, its application in industrial production is becoming more and more extensive. Textile on-line defect detection based on machine vision technology has become an important development direction of textile quality control. The domestic research is mainly for a certain algorithm and only suitable for one kind of fabric defect detection. It can not be directly used in the production of glass fiber cloth defect detection. Therefore, the key technology of glass fiber cloth defect detection is studied. It is of great significance to promote the automatic production of glass fiber fabric and the rapid grading of fabric quality. In this paper, the key technology of glass fiber fabric defect detection system is studied deeply and systematically. The main contents are as follows: based on the texture characteristics, detection requirements and production environment of glass fiber cloth, the overall scheme of glass fiber cloth defect detection system is designed, and the machine vision testing platform of glass fiber cloth is built. According to the defect characteristics of glass fiber cloth, the light source configuration scheme of backlight illumination and the multi-camera detection scheme based on GigE are determined, and the high contrast fabric image is obtained. The difficulty of defect identification is reduced. Aiming at the problems of wide fabric width and small field of view of industrial CCD, In this paper, a practical scheme is proposed to synchronize the image acquisition with multiple cameras, and then the practical scheme of image stitching is proposed, which is based on template matching method and Harris feature point stitching method, respectively, and the glass fiber cloth image mosaic technology is studied respectively, which is based on the template matching method and the Harris feature point stitching method, respectively. The comparison and analysis of registration accuracy and stitching speed are also given in this paper, which is based on real-time and reliability. In order to solve the problems of low efficiency and poor real-time of on-line detection of glass fiber fabric, the paper chooses the mosaic method based on template matching to join the image of glass fiber cloth. In this paper, a fabric defect detection method based on Blob analysis is proposed. Firstly, the mean value filter is used to smooth the fabric image to reduce the noise and fabric texture interference. Then iterative method is used to find the best threshold value to segment the image into Blob and background pixel set. Morphological processing is used to adjust the Blob shape after segmentation. Finally, the connectivity analysis and feature extraction of the image are carried out. The number and size of defect features are obtained by fitting the Blob region with a minimum external rectangle, and the recognition of common defects such as glass fiber cloth cleavage, floral jump, hole breaking and stain is realized. The experimental results show that the method is simple and easy to calculate. The detection results are robust and real-time, and it is an effective on-line detection method for fabric defects. Based on the database of SQL and SQL Sever, a glass fiber fabric defect detection software system based on VS2010 platform is developed. The system includes image acquisition module. The man-machine interaction module, image processing module and defect data statistics module have realized the detection of glass fiber cloth defects and fabric grading. Debugging and experimental verification have been carried out on the experimental platform. The results show that the research method in this paper is stable and reliable. Good real-time, meet the expected research and development requirements.
【學位授予單位】：鄭州大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TS101.97;TP391.41

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