基于灰度形态重构的颗粒图像分割方法

提出了一种基于距离变换、形态重构和分水岭算法的图像分割算法。将一幅图像通过距离变换得到距离灰度图,与形态重构算法结合,得到颗粒图像的标识点图,用标识点图对距离灰度图进行分割,再用分水岭变换对分割后的距离灰度图进行变换。试验表明,该算法能有效合理地解决粘连或者重叠颗粒等物体的分割。     

基于快速高斯核函数模糊聚类算法的图像分割

对模糊聚类算法通过引入高斯核函数,平滑图像像素灰度值,从而增强图像分割的抗干扰能力和鲁棒性,并结合阈值模糊聚类算法,提高了图像分割的速度。首先利用阈值模糊聚类法划分初始输入空间,得到模糊规则数及初始聚类中心;然后用高斯核函数平滑图像的像素灰度值;最后用标准模糊聚类算法求解并优化模糊隶属度和聚类中心。将本算法应用于添加噪声的嫦娥一号采集的月球地面灰度图像和Lena灰度图像进行图像分割,仿真结果验证了本方法的鲁棒性、有效性和实用性。     

轮胎动态接地压力分布的光吸收法测量研究

设计光吸收法试验台架,通过图像处理算法及对负荷、充气压力、外倾角、侧偏角的控制,获得轮胎动态接地压力分布。图像处理算法如下：拍摄轮胎接地印痕图像,通过图像矫正、灰度化、滤波去噪及图像分割获取图像中轮胎与路面接触区域,再通过校准机构获取图像灰度与轮胎接地压力的相关性,将轮胎接地印痕图像转化为接地压力分布图。以规格为205/55R16的非对称花纹子午线轮胎为例,利用试验台架对轮胎动态接地印痕进行测量,获取不同工况下轮胎接地压力分布及接地印痕特征值,轮胎计算负荷相对误差绝对值基本小于10%,验证了光学试验台架的有效性。     

基于图论最小割集算法的图像分割研究

为了提高图像分割的质量,采用图论最小割集算法进行了研究。首先将图像中的像素点映射为图论节点,节点权值通过平衡因子与共享最近邻节点数的比率计算;然后基于最小化能量方程建立图像最小割集,提取分割块内的灰度值作为块特征向量,用最小生成树对图分割;接着用判定函数判断临近区域是合并或者分割;最后给出了算法流程。结果表明,该算法可以分割出目标信息,并且算法鲁棒性好、峰值内存小。     

基于机器视觉的图像缺陷检测的研究

对基于机器视觉的黑色O形橡胶圈缺陷检测系统展开了研究,对采集到的橡胶圈图像进行相关处理以便检测出缺陷特征,通过改进后的中值滤波滤除噪声,建立图像直方图了解缺陷特征的灰度分布情况,比较了几种边缘检测算子的检测效果,并通过实验仿真验证了算法的可行性。     

浮法玻璃缺陷在线检测识别方法研究

提出了一种基于机器视觉的分布式浮法玻璃缺陷在线检测识别方法。针对采集到玻璃图像为非均匀图像的特点,首先对图像进行平滑滤波处理,剔除信号波动带来的灰度尖峰和毛刺,然后设计了基于自跟踪的向下阈值曲面分割方法实现缺陷和背景的分割,实现了基于扫描线的图像搜索算法保证缺陷寻找的速度,并利用神经网络对缺陷进行分类。实际应用表明,该系统能够准确可靠的实现在线检测和识别浮法玻璃缺陷,满足了浮法玻璃企业对玻璃缺陷实时检测的需要。     

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。     

一种陶瓷瓶内壁裂纹检测算法

为了准确地对陶瓷瓶内壁进行裂纹检测,提出了一种基于数字图像处理技术的陶瓷瓶内壁裂缝检测算法。首先对所获取的陶瓷瓶内壁图片进行光照和去噪处理,并利用图像增强提高陶瓷瓶内壁图像的对比度;然后通过图像分割和连通域算法标记各连通域;最后通过对连通域进行检测来判断是否为裂纹,并将检测出的裂纹进行定位。仿真实验结果表明,所提出的算法能准确地检测出条形裂纹、横向裂纹、纵向裂纹等常见的陶瓷瓶内壁裂纹。     

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。     

基于改进流域变换的虹膜区域分割

虹膜图像异常区域分割是虹膜诊断的关键。由于虹膜图像纹理丰富,很多图像分割方法无法取得有效的分割结果。文中采用改进流域变换算法,有效地对虹膜图像进行分割,并避免了传统流域变换存在的过分割问题。试验结果表明,改进流域变换分割虹膜图像异常区域,精确地提取出了异常区域边缘,且无需分割后的区域合并处理,降低了分割的复杂性。     

A nondestructive testing method for detecting surface defects of Si3N4-Bearing cylindrical rollers based on an optimized convolutional neural network

Aiming at solving the problems of low detection efficiency, poor accuracy rate, and low applicability of the traditional detection methods for surface defects on the silicon nitride (Si3N4) bearing cylindrical rollers. In this study, to detect the surface defects on Si3N4-bearing cylindrical rollers, a nondestructive testing (NDT) method based on an optimized convolutional neural network is proposed, which uses machine vision system to detect the surface defects. The optimized convolutional neural network is a two-stage network. It combines a semantic segmentation sub-network and a decision sub-network. The semantic segmentation sub-network performs an end-to-end learning based on the features of the original image. It completes defect feature extraction to segment the surface defect area from the normal area. The decision sub-network classifies the defects in the segmented surface defect area. The experimental results show that the detection time of surface defects on Si3N4-bearing cylindrical rollers by the proposed network was 72 ms. In addition, its Accuracy (Acc), Percision (Pre), Sensitivity (Sen), and Specificity (Spe) were 97.5%, 99.9%, 99.0%, 98.6%, respectively, which significantly improved the Acc and Pre of the detection of surface defects on Si3N4-bearing cylindrical rollers compared with the traditional detection method. On the other hand, its mIoU, mPA were 84.4%, 92.9%, respectively, meaning brilliant ability of image segmentation for this network. To sum up, the ability of the proposed network to detect and classify surface defects on Si3N4-bearing cylindrical rollers is excellent.     

基于机器视觉的塑料制品外观缺陷检测

为了克服传统人工检测方法效率低、自动化和智能化程度低等缺陷,设计了一种基于机器视觉的塑料制品外观缺陷检测方法,利用摄像机进行制品外观缺陷检测,实现制品生产流水线的自动化、智能化检测.介绍了机器视觉检测系统组成.为了提高机器视觉检测系统准确率,对采集到的图像进行了均值滤波和边缘分割检测.结果表明:机器视觉检测系统能够实现...     

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.     

基于变异系数法的工业产品表面缺陷快速检测应用研究

为了提高工业产品质量控制中表面缺陷检测的准确性和快速性,提出了一种基于机器视觉的工业产品表面缺陷快速检测方法。方法引用变异系数的概念,通过待检测图像和模板图像进行差运算得到差影图像,并通过计算其变异系数以确定阈值,利用分割定位缺陷,从而实现缺陷的快速检测。壁纸表面缺陷检测实验结果表明,本方法提高了图像检测系统的准确性和鲁棒性,误检率大大降低。     

带有收敛因子的PSO-Fisher图像分割的方法

在大多数的实际应用中,仅仅将图像分成目标和背景两种类型是远远不够的。然而要实现三类、四类以致多类的分割,就必须研究多阈值的分割方法。以研究Fisher单阈值分割法为基础,利用带有收敛因子的PSO算法将Fisher评价函数来进行两阈值分割,最终推广到多阈值的基于Fisher评价函数的图像分割。并通过定误差图像分割实验证明了方法的有效性,保证了算法的收敛。同时证明了选择一个合适的粒子群规模可以使得带有收敛因子的PSO-Fisher算法在尽量少的迭代次数下提高算法的成功率。     

采用RFR-GA算法的薄壁注塑件质量多目标优化

汽车内饰件可由注塑加工获得,但成型过程中塑件产生的翘曲、体积收缩较大,针对该问题,以某汽车薄壁注塑件为例,研究了其注塑工艺参数的优化方法.通过以注塑过程中的最小翘曲和最小体积收缩率为目标函数,以注塑温度、模具温度、注射压力、保压压力、保压时间以及冷却时间等参数作为设计变量,构建了多目标全局优化模型.利用Moldflow...     

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%.     

状态估计与不良数据检测方法及其在大庆油田电网中的应用

为了解决常用的状态估计方法常易出现的发散现象,提出了基于改进粒子群算法的状态估计。同时由于系统存在不良数据,不能进行电力系统的常规计算,采用了基于检测法进行不良数据检测。把改进粒子群算法引入到状态估计求解非线性方程中,使得方程的解由不收敛和局部最优解转变为全局最优解。为了验证基于改进粒子群算法的状态估计的正确性和基于检测法进行不良数据检测的合理性,用IEEE9节点系统进行状态估计和不良数据检测的计算,并用编程语言实现了状态估计和不良数据检测的结果显示。结果表明基于改进粒子群算法的状态估计可以得到最优值,但时间较长,检测法可以检测出不良数据。然后对大庆油田电网的10个区分别进行了状态估计和不良数据检测的计算,以一区为例,选取了88个量测数据进行计算,得出了满意的结果,也证明了此方法的正确性。     

Three-dimensional reconstruction of ceramic membrane with internal defects based on ultrasound imaging technique applying triangular matrix-synthetic aperture focusing

To realize a three-dimensional (3D) visual display and evaluate the internal defects of a ceramic hollow plate membrane, a method for creating a 3D reconstruction of the internal defects based on an ultrasound imaging technique applying triangular matrix–synthetic aperture focusing is proposed. First, a phased array detector is used to scan the surface of the ceramic membrane, and ultrasound imaging is achieved based on the triangular matrix–synthetic aperture focusing technology. The acquired B-scan images are pre-processed and a 3D reconstruction is achieved based on the contours of the internal defects. Finally, a 3D visualization of the internal defects of a hollow plate ceramic membrane is realized. The use of a triangular matrix focusing method can improve the detection efficiency because the amount of data acquired and the number of calculations are reduced by nearly half. In addition, the synthetic aperture focusing method can ensure a high imaging resolution, which leads to an improved recognition accuracy of the defect size. As a result, the error of the diameter measurements of the inner hole defects ranges between 2% and 3%, and the error of the diameter and length measurements of the crack line defects ranges between 2% and 4%. Experimental results with real datasets are presented to demonstrate the efficiency and accuracy of our method as compared with traditional methods.