机器视觉在小型管道探伤中的应用研究

设计了针对小型管道内部缺陷检测的螺旋管道机器人系统,基于该机器人系统提出了图像处理的改进算法。首先采用结合中值滤波思想的双边滤波器,解决了双边滤波无法去除孤立噪声点的问题;其次采用了二维最大熵的阈值分割方法进行图像分割;最后根据管道缺陷的特点提取适合分类器分类的代表特征点进行分类。仿真研究表明:所提算法能够更加完整地提取缺陷信息。     

基于机器视觉的汽车零件涂装缺陷检测方法

设计了一种基于机器视觉的涂装缺陷检测系统来检测汽车零件表面的孔洞边缘缺陷,并在Halcon软件中实现了相关检测算法.首先搭建了检测平台获取图像信息,运用迭代加权拟合的方法实现了对孔洞感兴趣区域(ROI)的定位与提取,从而缩短检测时间;再使用一种带阻滤波器对图像进行预处理,减少了表面反光;最后运用阈值分割等方法对涂装缺陷特征进行识别和提取.该法能快速、准确地识别提取涂装缺陷特征,平均单次识别时间为320 ms,识别准确率为97％,满足工业涂装缺陷检测的要求.     

基于稀疏求解的输油管道焊缝图像缺陷检测算法研究

提出一种基于稀疏求解的输油管道焊缝图像缺陷检测算法,通过求解稀疏系数完成识别过程,避免了图像的分割与特征值的计算。将现场提取的缺陷和噪声图像作为样本,根据压缩感知理论,首先通过学习从样本中获得字典,利用相关性最小的原则确定字典矩阵数量,然后构建稀疏模型并采用正交匹配追踪算法求解该模型,最后通过求解得出的系数组合判断图像类型。实验表明:该方法能够实现图像缺陷的准确识别。     

基于机器视觉技术的塑料制品缺陷检测研究

阐述了机器视觉技术在塑料制品缺陷检测中的应用,详细说明了机器视觉系统图像处理过程及特征值提取过程。其中图像处理过程包括采用直方图规定法进行图像增强,采用双峰法进行图像阈值分割,采用Canny边缘检测算子提取塑料制品缺陷区域轮廓。特征值提取过程则包括形状特征、颜色特征及位置关系特征,以飞边、裂纹和短射三种缺陷为例进行了提取和区分。结果表明:细长度可用来区分出裂纹,灰度值与模板差可用来区分出短射,缺陷区域位置关系则可以区分出飞边。     

实验用玻璃器皿缺陷检测研究

针对实验玻璃器皿高质量要求,提出一种基于图像处理的实验室玻璃器皿缺陷检测方法。为更直观检测到玻璃器皿的缺陷,以实验室常用的西林瓶为例,首先对瓶口检测区域进行定位,将瓶口与背景图像分割,并利用最大类间方差法对图像进行填充,从而得到瓶口图像的定位;将定位好的瓶口图像通过极坐标变换和动态阀值分割,实现瓶口图像特征的提取,最后运用模板匹配法实现对瓶口图像缺陷的检测。结果表明,本缺陷监测方法可很好实现毛口和破口的识别,且在不同瓶口半径下的缺陷检测平均准确率可达97.4%,具有较高的识别准确率。     

基于机器视觉的电镀件表面缺陷检测系统

将机器视觉引入电镀件表面缺陷检测中,设计了一种基于机器视觉的电镀件表面缺陷检测系统。该系统由工控机、图像采集卡、工业相机、伺服电机、照明装置和运动控制卡组成。获取电镀件表面光学图像并经预处理后,利用算法提取出图像中缺陷区域边界特征,通过计算用于标记缺陷区域边界特征的白色像素点个数,并与设定的阈值做比较,实现电镀件表面缺陷检测。该系统满足电镀件表面缺陷在线检测的要求。     

中值改进的分割算法在钢缺陷检测中的应用

针对压力钢管内壁表面缺陷图像在直方图呈偏态或重尾时图像分割效果差的问题,在传统的图像分割思想的基础上,提出了一种基于灰度中值和灰度平均离差分生成图像最佳分割阈值主变量的改进算法。实验结果表明,改进后的算法对于能够稳定有效地分割压力钢管内壁正常部位与缺陷部位,并将缺陷部位的细节特征增强显示,为后续分析缺陷原因提供了很好的形态学依据;显示了其在化工设备的完整性检测技术中的应用意义。     

基于机器视觉的磁控溅射薄膜件的划痕检测

针对目前磁控溅射薄膜件人工检测效率低的问题,结合企业需求,设计了一种基于机器视觉技术的磁控溅射薄膜件划痕自动检测系统,实现对产品表面各种划痕的自动检测。首先对采集到的图像进行校正,利用标准图像创建模板,通过形状模板匹配提取样本图像中感兴趣的区域。然后将图像分解成低频和高频两部分,对低频部分进行先膨胀后腐蚀的操作,对高频部分进行中值滤波以去除噪声,图像合成后通过取反和灰度缩放生成增强图像;最后采用二维Otsu拟合线阈值分割法对图像进行分割,采用选取指定形状特征和骨架提取法对划痕缺陷进行提取和测量。对100件样品进行多次随机验证的结果表明,本文设计的系统能快速、准确、高效地提取并测量划痕缺陷,每个样品检测的平均耗时仅0.25 s,正检率达98.5%。该方案与人工检测比,速度更快、准确度更高,可以满足工业应用的需求。     

一种基于频域变换的无监督车身漆膜缺陷检测算法

针对现有的2D汽车漆膜缺陷检测算法不能很好地检测出颜色暗、对比度低和较小的漆膜缺陷的问题,提出了一种基于频域变换的漆膜缺陷检测算法。首先通过对其主频率进行零掩码来消除图像背景中频谱显示较高梯度值分量的图像背景噪声。然后使用小波收缩法对傅里叶逆变换后的图像进行去噪。通过小波系数进行阈值修改,提取缺陷的高频信息,最后对重建后的图像使用阈值分割法分割出缺陷区域。对不同尺度和类型缺陷样本进行实验的结果表明,该算法检测准确率为95.01%,且具备良好的鲁棒性。     

陶瓷砖表面缺陷视觉检测算法研究

提出了一种基于灰色关联分析的特征颜色提取算法,在此基础上,完成了陶瓷砖表面缺陷检测软件设计。应用灰色关联分析的方法来确定像素点间的隶属度,对图像进行分块处理来求解初始特征颜色,使用灰色聚类进行图像分割。系统以ARM为工作平台,工作在RGB颜色空间。实验表明:算法可快速有效地完成陶瓷砖表面缺陷的检测。     

The design of optimal real Gabor filters and their applications in fabric defect detection

Fabric defect detection has been recognised as one of the key challenges for automatic production, and Gabor filters are one of the most useful tools in detecting fabric defects. The half‐peak tangent method is applied in real Gabor filter design so that the filters can cover the frequency of defects as much as possible. Meanwhile, the half‐peak‐magnitude contours of the neighbouring filters are tangential. On this basis, two optimal orientations are selected by applying direction masks, and the optimal scale at each optimal orientation is determined according to the signal‐to‐noise ratio. In this way, two optimal real Gabor filters are obtained. A new algorithm based on the two optimal filters is proposed for fabric defect detection. A series of experiments are carried out for 46 fabric defect images combined with 46 corresponding reference fabric images, in order to verify the effectiveness of the new algorithm. The experimental results obtained show that the new algorithm can accurately detect defects in grey fabric defect images as well as in colour images. For the 46 fabric defect images, the detection rate is 95.66%, indicating that the new algorithm performs well. In addition, comparison of the new algorithm with other algorithms in the literature demonstrates that the new algorithm is more effective in the detection of several fabric defect images.     

An enhancement algorithm based on adaptive updating template with Gaussian model for Si3N4 ceramic bearing roller surface defects detection

The method based on machine vision image processing is used to detect the surface defects of Si₃N₄ bearing roller. Owing to the variety of defects, small area and low contrast, it is easy to miss or error detection. In this paper, an adaptive update template defect enhancement algorithm based on Gaussian model is proposed. First, a large number of surface images of Si₃N₄ bearing roller are collected to obtain the non-defect background statistical feature, and the background characteristic curve is fitted by Gaussian model. Further, the initial background template is gained according to the Gaussian curve. Then, combined with the gray distribute of defect images and initial background template, unique adaptive update template can be established. Finally, subtraction operation and nonlinear enhancement are used to improve the comparison of defect information and background. Through inverse sorting, adaptive threshold segmentation and Canny operation, the precise positioning of defects is realized. The enhancement algorithm can effectively enhance the contrast and eliminate the influence of noise. The average detection time is 0.84s, and the detection accuracy is 96.2%.     

轮胎胎面花纹边界特征提取方法研究

在对轮胎花纹造型特征研究的基础上,对胎面花纹边界特征提取在逆向数字化建模中的应用方法进行研究。在空间切片分层和投影图像特征提取的基础上,提出了一种基于灰度图像区域增长的边界特征提取方法。该方法首先借鉴切片分层的思想,采用同心球与扇形混合空间分割方案以及主成分统计分析方法,将三维扫描点云投影为带有深度值的胎面花纹灰度图像,然后利用区域增长方法提取出花纹边界特征点。试验结果表明,该方法能够处理各种复杂的半钢子午线轮胎花纹,可在约50s内完成360°3D花纹的特征点提取,提取误差较小。该方法从底层解决了逆向工程技术在轮胎行业中的应用,在对诸多新型轮胎花纹测试中具有较好的精确性、高效性及广泛的自适应性,能有效地缩短开发周期并改善轮胎的综合性能。     

基于互信息的PCA方法及其在过程监测中的应用

主元分析(PCA)是一种经典的特征提取方法,已被广泛用于多变量统计过程监测,其算法的本质在于提取过程数据各变量之间的相关性。然而,传统PCA算法中定义的相关性矩阵局限于计算变量间的线性关系,无法衡量两个变量间相互依赖的强弱程度。为此,提出一种新的基于互信息的PCA方法(MIPCA)并将之应用于过程监测。与传统PCA所不同的是,MIPCA通过计算两两变量间的互信息来定义相关性,将原始相关性矩阵取而代之为互信息矩阵,并利用该互信息矩阵的特征向量实现对过程数据的特征提取。在此基础上,可以建立相应的统计监测模型。最后,通过实例验证MIPCA用于过程监测的可行性和有效性。     

A smoke segmentation algorithm based on improved intelligent seeded region growing

Image segmentation method based on region growing has the advantages of simple segmentation method and complete segmentation target. Because the color discrimination and gray gradient of smoke are not obvious, the traditional region growing segmentation method is difficult to separate it from the image, resulting in an unsatisfactory segmentation effect. To solve this problem, this paper partially improved the region growing method and proposed a new smoke segmentation algorithm based on the improved intelligent seeded region growing (IISRG) method. First, smoke images obtained from experimental videos were converted from the RGB color space to the HSV color space, and image binarization was achieved using background subtraction with an adaptive threshold in the V channel. Then, a pixel in the binary image was selected intelligently as the seed point, which was used for the regional growth. The final smoke segmentation images were obtained by the morphological processing of region growing images. Experimental smoke segmentation results show that the proposed algorithm has a higher overlap rate and a lower overflow rate, and performs a better smoke segmentation effect compared with the other two approaches. In addition, this algorithm can also effectively solve the problems of under‐segmentation and over‐segmentation of smoke images.     

基于ERT传感器和数据挖掘技术的空气水两相流空隙率测量(英文)

Based on an electrical resistance tomography(ERT) sensor and the data mining technology,a new voidage measurement method is proposed for air-water two-phase flow.The data mining technology used in this work is a least squares support vector machine(LS-SVM) algorithm together with the feature extraction method,and three feature extraction methods are tested:principal component analysis(PCA),partial least squares(PLS) and independent component analysis(ICA).In the practical voidage measurement process,the flow pattern is firstly identified directly from the conductance values obtained by the ERT sensor.Then,the appropriate voidage measurement model is selected according to the flow pattern identification result.Finally,the voidage is calculated.Experimental results show that the proposed method can measure the voidage effectively,and the measurement accuracy and speed are satisfactory.Compared with the conventional voidage measurement methods based on ERT,the proposed method doesn’t need any image reconstruction process,so it has the advantage of good real-time performance.Due to the introduction of flow pattern identification,the influence of flow pattern on the voidage measurement is overcome.Besides,it is demonstrated that the LS-SVM method with PLS feature extraction presents the best measurement performance among the tested methods.     

光谱图像分析法在镀层成分识别中的应用

镀层成分识别可以为镀液配方改进和电镀工艺优化提供参考依据。提出了一种基于光谱图像分析的镀层成分识别方法。通过获取镀层光谱图像信息,应用小波分解实现特征提取,采用光谱图像分析方法并结合图像处理算法,对镀层成分进行识别。设计了镀层成分识别系统,介绍了该识别系统的硬件结构,阐述了镀层的光谱图像特征提取与成分识别过程,并进行了实验与分析。     

Attention-based vector quantisation variational autoencoder for colour-patterned fabrics defect detection

Defect detection is an essential link in the fabric production process. Due to the diversity of patterns and scarcity of defect samples for colour-patterned fabrics, reconstruction-based unsupervised deep learning algorithms have received extensive attention in the field of fabric defect detection. Among them, unsupervised reconstruction models based on variational autoencoders (VAEs) have been shown to be effective. However, there is a problem of posterior collapse in the process of modelling parametric distributions of continuous variables by VAEs. Therefore, VAE-based defect detection methods for colour-patterned fabrics usually produce ambiguous reconstruction results, thereby affecting the defect detection performance. In this article, an attention-based vector quantisation variational autoencoder (AVQ-VAE) is proposed for colour-patterned fabric defect detection. The method adopts autoregressive modelling of discrete variables to avoid the posterior collapse problem of traditional VAEs, and utilises attention mechanism to enhance the feature representation ability of the model. AVQ-VAE consists of encoder, embedding space, decoder and attention mechanism. The encoder is used to map the input image into multiple feature vectors. Vector quantisation in embedding space is used for discretisation and autoregressive modelling of feature vectors. A decoder is used to decode discrete variables into images of the same size as the original input. Furthermore, an attention mechanism is used to capture channel and spatial correlations, which help the model focus on important information by adaptively recalibrating feature maps. Experimental results on public datasets demonstrate that the proposed method is robust and effective for colour-patterned fabric defect detection.     

Determination of steel quality based on discriminating textural feature selection

Image data can be acquired from a product surface in real time by image sensor systems in chemical plants. For quality determination based on these image datasets, effective texture classification methodology is essential to handle highly dimensional images and to extract quality-related information from these product surface images.Wavelet texture analysis is useful for reducing the dimension and extracting textural information from images. Although wavelet texture analysis extracts only textural characteristics from images, the extracted features still contain unnecessary information for classification. The texture analysis method can be improved by retaining only class-dependent features and removing common features. In previous works, best basis and local discriminant basis are the most popular techniques for selecting an important basis from the wavelet packet basis. However, feature selection based on wavelet texture analysis has been studied for texture classification. Because previous methods are designed for wavelet coefficients with features for analysis, their performance is poor with wavelet texture analysis.We propose a novel texture classification methodology for quality determination based on feature selection using wavelet texture analysis. The proposed methodology applies the sequential forward floating selection (SFFS) algorithm as a feature selection strategy to select discriminating wavelet signatures using wavelet texture analysis. The proposed methodology is validated through quality determination for industrial steel surfaces. The results show that the proposed method has fewer classification errors with fewer number of features than previous methods.     

Colour metrics for image edge detection

Image edge detection based on low-level feature is usually performed on gray-scale images. Some methods have been developed for edge detection on colour images based on low-level feature, but they are not consistent with human colour perception. This research provides a new algorithm for edge detection based on the “HyAB” large-colour-difference formula. This algorithm uses Sobel operators for gradient-magnitude calculations and Canny methods for localizing edge points. The performance of the new algorithm is qualitatively compared with Sobal and Canny methods using some challenging colour images. The results indicate that gradient magnitudes are best calculated using the HyAB colour-difference formula, and that CIELAB and CIEDE2000 differences are not suitable for this purpose. Definition of gradient magnitudes according human perception is essential in applications such as quality control of fabric printing, calculation of disruptive colouration, and so on. The new algorithm is successful in accuracy and fine edge detection in comparison with the Sobel and Canny methods. The new method is quantitatively compared with state-of-the-art methods using three datasets including BSDS500, MBDD, and BIPED. The correctness and accuracy of annotations of images in datasets have an important effect on results. The new method does not reach scores better than deep-learning-based methods, but it is simple and does not need training. It could probably have better results with improving noise-suppression.