焊縫缺陷檢測算法研究
發(fā)布時間：2018-07-13 13:23
【摘要】：隨著圖像處理技術(shù)和計算機(jī)技術(shù)的不斷發(fā)展,對X射線焊縫圖像的數(shù)字化起到了極大的推動作用。而目前X射線探傷檢測仍以人工評定方式為主,且在評定過程中易受個人主觀因素影響,工作量大,容易引起誤判、漏判,因此實(shí)現(xiàn)焊縫缺陷自動檢測十分必要。本文以DR系統(tǒng)采集的焊縫圖像為研究對象,主要圍繞焊縫缺陷檢測算法進(jìn)行研究。針對焊縫圖像中紋理復(fù)雜、對比度差、背景起伏大等問題,首先對圖像進(jìn)行了相應(yīng)的預(yù)處理算法研究;然后針對缺陷特點(diǎn)的不同,進(jìn)行了相應(yīng)的缺陷檢測方法研究;最后利用不同算法間的互補(bǔ)性,將結(jié)果融合避免誤檢和漏檢。首先,針對焊縫圖像中信噪比低、對比度差等問題,利用圖像增強(qiáng)和降噪改善圖像質(zhì)量、減少圖像中噪聲的干擾;針對動態(tài)視頻檢測,采用灰度歸一化和每M幀連續(xù)檢測N幀的方案分別解決了不同規(guī)格間灰度分布不一致和采集檢測實(shí)時顯示的問題;同時為了減少干擾和提高檢測效率,提出了一類針對DR成像的焊縫邊界自動提取方法,具有較好的適應(yīng)性和實(shí)用性。其次,針對焊縫缺陷的提取問題,根據(jù)不同焊縫缺陷的特點(diǎn),本文設(shè)計了相應(yīng)的基于Canny、Lapalace、幀差法、ButterWorth濾波的缺陷檢測算法,但同一方法只針對特定的缺陷類型效果明顯,為了防止缺陷漏報,利用不同檢測方法間的互補(bǔ)性,將檢測結(jié)果融合,并針對動態(tài)視頻和靜態(tài)圖片,設(shè)計了相應(yīng)的檢測方案,具有較好的通用性。本文通過對焊縫圖像缺陷區(qū)域與非缺陷區(qū)域的特性分析,利用空間特性構(gòu)建模式矢量,并利用SVM對樣本進(jìn)行訓(xùn)練,之后進(jìn)行缺陷檢測和相應(yīng)的結(jié)果分析。最后,本文設(shè)計并實(shí)現(xiàn)了基于DR成像及缺陷檢測系統(tǒng)。利用計算機(jī)多線程技術(shù)實(shí)現(xiàn)了圖像的采集、抓拍、缺陷檢測等功能。
[Abstract]:With the development of image processing technology and computer technology, the digitization of X-ray weld image has been greatly promoted. But at present, the main method of X-ray flaw detection is manual assessment, and it is easy to be affected by individual subjective factors in the process of evaluation, so it is easy to cause misjudgment and miss judgment, so it is very necessary to realize automatic detection of weld defects. In this paper, the weld image collected by Dr system is taken as the research object, mainly focusing on the weld defect detection algorithm. Aiming at the problems of complex texture, poor contrast and large background fluctuation in the weld image, the corresponding pre-processing algorithm is studied firstly, and then the corresponding defect detection method is studied according to the different characteristics of the defect. Finally, by using the complementarities of different algorithms, the results are fused to avoid false detection and miss detection. Firstly, aiming at the problems of low signal-to-noise ratio and poor contrast in weld image, image enhancement and noise reduction are used to improve image quality and reduce noise interference. In order to reduce interference and improve detection efficiency, gray level normalization and continuous detection of N frames per M frame are adopted to solve the problems of inconsistent gray distribution and real-time display of acquisition and detection among different specifications, respectively. A method for automatic extraction of weld boundary for Dr imaging is presented, which has good adaptability and practicability. Secondly, aiming at the problem of weld defect extraction, according to the characteristics of different weld defects, this paper designs the corresponding defect detection algorithm based on Canny Lapalace, frame difference filter and ButterWorth filter, but the same method is effective only for specific defect types. In order to prevent defects from underreporting and to make use of the complementarity between different detection methods, the detection results are fused, and the corresponding detection scheme is designed for dynamic video and static pictures, which has good generality. By analyzing the characteristics of defect region and non-defect region of weld image, the pattern vector is constructed by using spatial characteristics, and the samples are trained by SVM, then defect detection and corresponding result analysis are carried out. Finally, this paper designs and implements the imaging and defect detection system based on Dr. The functions of image acquisition, capture, defect detection and so on are realized by computer multi-thread technology.
【學(xué)位授予單位】：西安理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TG441.7;TP391.41
本文編號：2119518
[Abstract]:With the development of image processing technology and computer technology, the digitization of X-ray weld image has been greatly promoted. But at present, the main method of X-ray flaw detection is manual assessment, and it is easy to be affected by individual subjective factors in the process of evaluation, so it is easy to cause misjudgment and miss judgment, so it is very necessary to realize automatic detection of weld defects. In this paper, the weld image collected by Dr system is taken as the research object, mainly focusing on the weld defect detection algorithm. Aiming at the problems of complex texture, poor contrast and large background fluctuation in the weld image, the corresponding pre-processing algorithm is studied firstly, and then the corresponding defect detection method is studied according to the different characteristics of the defect. Finally, by using the complementarities of different algorithms, the results are fused to avoid false detection and miss detection. Firstly, aiming at the problems of low signal-to-noise ratio and poor contrast in weld image, image enhancement and noise reduction are used to improve image quality and reduce noise interference. In order to reduce interference and improve detection efficiency, gray level normalization and continuous detection of N frames per M frame are adopted to solve the problems of inconsistent gray distribution and real-time display of acquisition and detection among different specifications, respectively. A method for automatic extraction of weld boundary for Dr imaging is presented, which has good adaptability and practicability. Secondly, aiming at the problem of weld defect extraction, according to the characteristics of different weld defects, this paper designs the corresponding defect detection algorithm based on Canny Lapalace, frame difference filter and ButterWorth filter, but the same method is effective only for specific defect types. In order to prevent defects from underreporting and to make use of the complementarity between different detection methods, the detection results are fused, and the corresponding detection scheme is designed for dynamic video and static pictures, which has good generality. By analyzing the characteristics of defect region and non-defect region of weld image, the pattern vector is constructed by using spatial characteristics, and the samples are trained by SVM, then defect detection and corresponding result analysis are carried out. Finally, this paper designs and implements the imaging and defect detection system based on Dr. The functions of image acquisition, capture, defect detection and so on are realized by computer multi-thread technology.
【學(xué)位授予單位】：西安理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TG441.7;TP391.41
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