共查询到20条相似文献,搜索用时 15 毫秒
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In this paper, we describe an automatic detection system to recognise welding defects in radiographic images. In a first stage, image processing techniques, including noise reduction, contrast enhancement, thresholding and labelling, were implemented to help in the recognition of weld regions and the detection of weld defects. In a second stage, a set of geometrical features which characterise the defect shape and orientation was proposed and extracted between defect candidates. In a third stage, an artificial neural network (ANN) for weld defect classification was used. With the aim of obtaining the best performance of ANN three different methods for improving network generalisation was used. The results was compared with a method without generalisation. For the input layer, the principal component analysis technique was used in order to reduce the number of feature variables; and, for the hidden layer, a different number of neurons was used. 相似文献
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This paper presents a new approach for weld defect identification from radiographic images. This approach is based on the generation of a database of defect features using Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients extracted from the Power Density Spectra (PDSs) of the weld segmented areas after performing pre-processing and segmentation stages. Artificial Neural Networks (ANNs) are used for the feature matching process in order to automatically identify defects in radiographic images. The performance of the proposed approach is evaluated using 150 radiographic images in the presence of various types of noise and blurring. The experimental results show that the proposed approach can be used in a reliable way for automatic weld defect identification from radiographic images in noisy environments, and can achieve high recognition rates. 相似文献
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针对焊接过程产生的缺陷,提出一种磁光成像传感的模糊灰度变换和滤波反投影(FGT-FBP)重构检测方法. 研究焊接缺陷的几何特征,通过分析裂纹和未熔合两种不同焊接缺陷在交变磁场励磁下的磁光成像特征,设计模糊规则,对磁光图像进行模糊灰度变换. 增强磁光图像对比度,使焊接缺陷形态趋势可视化,实现描述磁光成像焊接缺陷细节的无参考型图像评估方法. 对FGT处理的焊接缺陷磁光图进行旋转投影,并经过快速傅里叶变换和改进的滤波器进行滤波去噪,消除伪影后进行反投影变换实现焊接缺陷图像的重构. 利用滤波反投影重构算法进行去噪,可有效突出焊接缺陷特征. 最后结合阈值分割和边缘检测实现焊接缺陷检测. 结果表明,该方法能较准确检测裂纹和未熔合两种焊接缺陷. 相似文献
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In this paper, we describe an adaptive-network-based fuzzy inference system to recognise welding defects in radiographic images. In a first stage, image processing techniques, including noise reduction, contrast enhancement, thresholding and labelling, were implemented to help in the recognition of weld regions and the detection of weld defects. In a second stage, a set of 12 geometrical features which characterise the defect shape and orientation was proposed and extracted between defect candidates. In a third stage, an adaptive-network-based fuzzy inference system (ANFIS) for weld defect classification was used. With the aim of obtaining the best performance to automate the process of the classification of defects, of all possible combinations without repetition of the 12 features chosen, four were used as input for the ANFIS. The results were compared with the aim to know the features that allow the best classification. The correlation coefficients were determined obtaining a minimum value of 0.84. The accuracy or the proportion of the total number of predictions that were correct was determined obtaining a value of 82.6%. 相似文献
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对双面焊焊缝的缺陷自动检测,提出了对焊缝重叠区边缘区与非边缘区分别进行处理、非细长缺陷与细长缺陷分别进行处理的思路.在逐列灰度波形分析检出焊缝外边缘和两焊缝重叠区边缘的基础上,对于非细长缺陷,采用大模版中值与均值滤波相结合模拟背景,然后对消除背景后图像采用分区域阈值检出焊缝缺陷,对于细长缺陷,提出了基于逐列自适应二值化和改进霍夫变换的缺陷分割算法.结果表明,所提出的方法能够在有效地避免两焊缝重叠区边缘处误检的同时,检出微弱线缺陷. 相似文献
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Estimated accuracy of classification of defects detected in welded joints by radiographic tests 总被引:1,自引:0,他引:1
Romeu R. da Silva Marcio H.S. Siqueira Marcos Paulo Vieira de Souza Joo M.A. Rebello Luiz P. Calba 《NDT & E International》2005,38(5):335-343
Over the last two decades, there has been a considerably increase in the number of publications of research projects for the detection and classification of welding defects in radiographs using image processing and pattern recognition tools. All these research projects aim to set up an automatic or semi-automatic classification system for weld joint defects detected by the radiographic method. A classification system as such would allow a reduction in some inherent inexactnesses that occur in the conventional method, consequently increasing the precision of the report. This work is a study to estimate the accuracy of classification of the main classes of weld defects, such as: undercut, lack of penetration, porosity, slag inclusion, crack or lack of fusion. To carry out this work nonlinear pattern classifiers were developed, using neural networks. Also the largest number of radiographic patterns as possible was used as well as statistical inference techniques of random selection of data with (Bootstrap) and without repositioning in order to estimate the accuracy of the classification. The results pointed to an estimated accuracy of around 80% for the classes of defects analyzed. 相似文献
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机器人焊接因零件形状不规则和焊接工艺复杂不可避免带来各种焊缝缺陷. 针对二维主成分分析应用于焊缝表面缺陷检测时面临计算复杂度高、分类准确率低及无法进行增量学习等问题,提出了一种基于均值更新的增量二维主成分分析(mean updated incremental two-dimensional principal component analysis,MUI2DPCA)算法,并将MUI2DPCA和前馈神经网络( feedforward neural network,FNN)相结合进行焊缝表面缺陷在线检测. 首先,对相机捕获的视频帧图像进行预处理得到焊缝局部块图像. 然后,利用MUI2DPCA在线提取局部块图像的模式特征. MUI2DPCA对图像的特征主成分进行增量迭代估计,降低计算复杂度,并且能够增量更新当前的样本均值,减少无关特征变化对主成分收敛性的影响. 最后,利用FNN建立提取的模式特征与焊缝类别之间的联系,实时返回焊缝表面缺陷的检测信息. 试验结果表明,该检测方法平均分类准确率为95.40%,平均处理速度可达29帧/s,能够满足焊缝在线检测的实时性要求. 相似文献
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In ultrasonic time of flight diffraction (TOFD) D-scan image, only a small fraction represents defects, whereas the majority is redundant. Because of the low contrast between defect and background image, it is difficult to manually interpret TOFD image. In addition, due to the nature of the weak amplitude of ultrasonic diffracted signals, the human factor introduces inconsistency into the interpretation. In order to automatically distinguish weld defects from the D-scan image, a defect detection method based on image processing technique is proposed. First, image pre-processing including clutter and noise suppression is conducted. Second, information entropy based image segmentation technique is employed to extract defects in the pre-processed image. At last, mathematical morphology based post-processing is carried out. The experimental results show that with the proposed method, TOFD can be used for automatic weld defect detection with satisfactory level of reliability. 相似文献
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Haniza Yazid H. Arof Hafizal Yazid Sahrim Ahmad A.A. Mohamed F. Ahmad 《NDT & E International》2011,44(7):563-570
Automated detection of welding defects in radiographic images becomes nontrivial when uneven illumination, contrast and noise are present. In this paper, a new approach using surface thresholding method is proposed to detect defects in radiographic images of welding joints. In the first stage, several image processing techniques namely fuzzy c means clustering, region filling, mean filtering, edge detection, Otsu thresholding, and morphological operations method are utilized to locate the area where defects might exist. This is followed by the construction of the inverse thresholding surface and its implementation to locate defects in the identified area. The proposed method was tested on 60 radiographic images and it obtained 94.6% sensitivity. Its performance is compared to that of the watershed segmentation, which obtained 69.6%. 相似文献
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为了实现对焊缝表面缺陷的自动检测与分类,研究一种有效识别焊缝表面缺陷的激光视觉检测方法.通过激光视觉传感器采集焊缝图像并进行预处理,包括图像分割,灰度化,平滑去噪以及焊缝轮廓提取.采用方向梯度直方图(Histogram of Oriented Gradient, HOG)提取焊缝激光条纹轮廓图像的特征向量.其次,基于5折-交叉验证网格搜索方法进行模型参数寻优,最终建立了支持向量机(Support Vector Machine, SVM)智能模型识别与分类焊缝表面缺陷.通过调整焊缝轮廓提取算法、HOG特征维度得到不同特征数据并进行对比、分析焊缝缺陷的识别效果.在相同试验条件下,发现支持向量机比随机森林分类器、K最近邻分类器以及朴素贝叶斯分类器的识别率更高,达到97.86%.基于HOG-SVM的焊缝表面缺陷智能识别方法可有效提高焊缝缺陷(气孔、凹陷、咬边)及无缺陷的分类精度. 相似文献
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H. Kasban O. Zahran H. Arafa M. El-Kordy S.M.S. Elaraby F.E. Abd El-Samie 《NDT & E International》2011,44(2):226-231
This paper presents a new approach for feature extraction from radiography images acquired with gamma rays in order to detect weld defects. In this approach, images are lexicographically ordered into 1D signals. Then, Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients are extracted from these signals, one of their transforms, or both of them. Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) are tested and compared for efficient feature extraction. Neural networks are used for feature matching in the proposed approach. Sixteen radiography images containing seventy three weld defects are used to evaluate the performance of the proposed approach. For performance evaluation, the tested images are degraded by Gaussian, impulsive, speckle, or Poisson noises with and without blurring. The experimental results show that the proposed approach can be used in a reliable way for automatic defect detection from radiography images in the presence of noise and blurring. 相似文献
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焊缝缺陷影响结构安全,缺陷定性是实现结构安全评价的重要基础.研究了一种基于一维局部二元模式(one-dimensional local binary pattern,1-D LBP)算法结合核主成分分析(kernel principal component analysis,KPCA)提取焊缝缺陷回波信号特征的方法.采用1-D LBP算法提取缺陷回波信号的LBP特征,通过KPCA对此LBP特征集进行主成分分析,选取贡献率之和超过90%的前N个主成分作为缺陷分类的特征向量,利用基于径向基核函数的支持向量机(support vector machine,SVM)实现了缺陷类型的自动分类.以夹渣、气孔和未焊透三类焊缝缺陷为对象,开展了缺陷特征提取及分类试验.结果表明,使用LBP-KPCA特征进行缺陷分类时,准确率达到96.7%,优于常规特征,为焊缝缺陷分类及无损评价提供了重要参考. 相似文献
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In this paper, the X-ray nondestructive test method of small defects in precision weldments with complex structure was presented. To resolve the difficulty of defect segmentation in variable grey image, the image processing based on Visual Basic programming method was adopted. The methods of automatic contrast and partial grey stretch were used to enhance the X-ray detection image which has relatively low contrast, then automatic threshold method was carried out to segment the two high intensity zones, and weld zones which contain the small defects was extracted. Smoothing and sharpen processing were proceeded on the extracted weld zones, and small defects in X-ray detection image of weldments with complex structure were segmented by using the method of background subtraction in the end. The effects of raster were eliminated, and because of that the image processing was only proceeded on the extracted weld zones, the calculated speed using the above provided algorithm was improved. 相似文献