焊缝X射线探伤图像自动搭接检测方法

针对焊缝探伤图像造假的现象在工程中比较普遍,而目前主要通过人工读片方式进行检查,检查效率低下、容易漏检。提出了一种基于图像处理的方法对焊缝的搭接区域进行自动定位、分割和比对,建立了焊缝探伤图像搭接自动检测系统,检测是否存在焊缝图像造假的现象。基于拉东变换找到搭接定位标记,从焊缝图像中切割出搭接区图像,采用尺度不变特征变换算法进行搭接匹配,计算两个搭接区图像的结构相似度。最后根据SIFT关键点的匹配点数和结构相似度判断两个搭接区图像是否匹配。实验结果表明,该方法的焊缝图像搭接自动检测准确率达到87.3%。     

Patch-Based Weld Defect Segmentation and Classification Using Anisotropic Diffusion Image Enhancement Combined with Support-Vector Machine

Russian Journal of Nondestructive Testing - Several factors such as low image quality, limited human eye resolution, and biased interpretation make the manual radiography testing defect detection...     

基于模糊推理的X射线图像焊缝提取

对于航天、管路、压力容器等重要焊接结构，X射线焊缝底片的检测要求较高，工作量大，且检测结果直接受检测者主观因素的影响而不稳定。为了提高评片结果的规范性和一致性，实现焊缝底片评定过程的自动化很有必要。本文提出一种基于模糊理论的焊缝识别方法，应用模糊推理对图像逐线进行处理，根据空间对比度和空间方差确定焊缝边缘。使用该方法对一百多张铝合金焊缝X射线底片进行焊缝识别，成功提取出了所有的焊缝。     

球罐对接焊缝中裂纹、夹渣相控阵识谱技术探讨

选取球罐对接焊缝中常见的面状和体积状缺陷各一例(裂纹和夹渣),在试板中预埋这两种性质的自然缺陷,采用相控阵超声技术对其进行检测,分析两种缺陷的图谱特点,同时采用射线及TOFD检测技术对缺陷尺寸进行对比,为在实际检测中对该类缺陷的图谱识别及定量分析提供帮助。     

The Application of an ANFIS and Grey System Method in Turning Tool-Failure Detection

The cutting process is a major material removal process; hence, it is important to search for ways of detecting tool failure. This paper describes the results of the application of an adaptive-network-based fuzzy inference system (ANFIS) for tool-failure detection in a single-point turning operation. In a turning operation, wear and failure of the tool are usually monitored by measuring cutting force, load current, vibration, acoustic emission (AE) and temperature. The AE signal and cutting force signal provide useful information concerning the tool-failure condition. Therefore, five input parameters of the combined signals (AE signal and cutting force signal) have been used in the ANFIS model to detect the tool state. In this model, we adopted three different types of membership function for analysis for ANFIS training and compared their differences regarding the accuracy rate of the tool-state detection. The result obtained for the successful classification of tool state with respect to only two classes (normal or failure) is very good. The results also indicate that a triangular MF and a generalised bell MF have a better rate of detection. We also applied grey relational analysis to determine the order of influence of the five cutting parameters on tool-state detection.     

Classification Method Research to Remote Sensing Images

With rapid development of remote sensing technology,the resolution of remote sensing images is increasingly improved;then people can extract more useful data and information from these images.Thus,an important information extraction method from remote sensing images - image classification,becomes more and more important.Based on phenophase and band composition characteristics,this paper firstly discusses the important role of background parameters in remote sensing images classification;then based on geographical information system technology,the computerized automtic classification to high-medium-low-yield croplands in Dingxiang County of Shanxi Province in remote sensing images has been carried out by using compound layers classification method of multi-thematic information;compared the classification result to the visual interpretation results,the accuracy increases from 70% to above 90%.     

Segmentation of Weld Defects Using Multiphase Level Set by the Piecewise-Smooth Mumford-Shah Model

Russian Journal of Nondestructive Testing - This paper deals with the problem of the X-ray image segmentation used to detect weld defects for a non-destructive testing task. In this work we have...     

复合板塔器焊缝裂纹的返修

对复合板塔器产生裂纹的原因进行分析,并结合其焊接特点制订了返修方案.在方案中充分考虑到现场的条件以及塔器的整体安全,并在施工过程中进行了合理的调整.详细的方案和认真的施工保证了返修的一次成功.对分析复合板设备缺陷的产生及其返修有一定的借鉴.     

Deep Learning Based Simple CNN Weld Defects Classification Using Optimization Technique

Russian Journal of Nondestructive Testing - Weld fault identification using X-ray pictures is a useful nondestructive testing technique. Traditionally, this job has relied on skilled human experts,...     

一种强粘连细胞图像的分割方法与实现研究

当多个细胞严重粘连时,传统的分割方法难以取得理想的分割效果。针对大部分细胞呈近似圆形的特点,提出了一种基于Hough变换圆检测的粘连细胞分离方法：采用彩色梯度法提取粘连目标边缘,然后针对细胞的圆形特征,以边缘有效像素点与对应圆周长的比值作为分割闽值做圆检测确定各细胞圆心及半径．并建立实体细胞判别准则作为粘连分割实现依据,最后根据各细胞有效弧含像素的多少确定对应圆的完整性实现粘连分割。实验表明,与采用传统的分水岭算法比较,该方法在强粘连细胞的分割方面,具有明显的优势。     

边缘检测算子在断口图像识别中的应用

边缘检测技术是图像处理和计算机视觉等领域最基本的技术。不同的微分算子对边缘的敏感程度是不同的,如何选用适当的边缘检测算子得到准确的边缘信息,就成为断口图像处理的关键步骤。通过M ATLAB进行仿真研究,比较和分析几个不同算子的边缘处理结果,从中得出C anny算子较适用于断口图像的识别与分类过程。     

基于一种改进最小误差算法的BGA图像分割

利用最小误差算法对图像分割前,首先要假设图像的灰度直方图呈高斯分布,而现实中图像灰度直方图并不完全符合高斯分布,这样在利用最小误差法对图像进行分割时就会造成一定误差.利用最大投影原理以高斯函数集作为基函数集,通过正交投影分解法确定高斯函数的参数,实现对BGA直方图的重构.重构后,应用最小误差算法对BGA图像进行了分割.实验表明该方法在分割BGA图像的空洞缺陷上是有效的.     

Segmentation of Computer-Processed Images of Tested Surfaces Obtained Using the Dye-Penetrant Testing

A segmentation method for analyzing images of tested surfaces obtained by computer processing results of penetrant-dye tests with the help of the technical vision system is suggested. The method is based on the processing of the recorded images using color filtering and the basic criteria used in visual analysis of tested surfaces after penetrant-dye tests.     

全焊透对接接头焊缝超声波检测方法分析

介绍了对接接头焊缝超声波检测的特点和要求,并提出了解决方法和需要注意的问题,以便日后工作中更好地检出缺陷,以保证工件质量和安全.     

基于分水岭的短裂纹统计技术

局域短裂纹数统计是疲劳短裂纹扩展研究的关键技术之一,本文提出并实现了一种基于分水岭算法的局域短裂纹数统计方法。针对传统分水岭算法容易产生过分割的缺点,在分析含短裂纹群材料试件锥束CT图像中短裂纹群的形态特征的基础上,综合运用一系列数学形态学方法进行预处理,有效抑制噪声,增强图像对比度,提取标记;之后,运用距离变换进一步优化预处理结果,最后,使用标记控制的分水岭算法实现分割并统计局域短裂纹数。实验结果证明,该算法能有效地抑制过分割现象,得到较好的统计结果,是实用、准确和有效的。     

透平压缩机壳体(HT250)裂纹补焊工艺

阐述采用灰口铸铁补焊工艺对透平压缩机壳体加强筋部位裂纹进行补焊,可有效防止裂纹产生,使焊缝有一定的塑性和强度,并有较好的抗裂性能和机加工性能。     

基于支持向量机的X射线焊缝缺陷检测

为提高对焊缝缺陷的检测精度,提出采用支持向量机(Support Vector Machine,SVM)分类的方法对X射线焊缝图像进行分割.选择训练样本图像的灰度、形态学梯度作为训练向量的特征分量对SVM进行训练,得到SVM分割模型后,将测试样本输入分割模型进行分割处理.以气孔缺陷为例,证明了该方法能实现焊缝气孔缺陷的准确分割,与其他分割方法相比,可提高缺陷检测的精度.     

接管内壁钴基合金堆焊层上裂纹的修复

通过对煤化工装置真空闪蒸罐中接管内壁钴基堆焊裂纹的修复,分析了裂纹产生的原因,拟定了合理的修复工艺参数及热处理措施,达到了预期效果,对钴基合金堆焊层上裂纹的修复,具有一定的指导意义。     

Vehicle Detection in Still Images by Using Boosted Local Feature Detector

Vehicle detection in still images is a comparatively difficult task.This paper presents a method for this task by using boosted local pattem detector constructed from two local features including Haar-like and oriented gradient features.The whole process is composed of three stages.In the first stage,local appearance features of vehicles and non-vehicle objects are extracted.Haar-like and oriented gradient features arc extracted separately in this stage as local features.In the second stage,Adaboost algorithm is used to select the mast discriminative features as weak detectors from the two local feature sets,and a strong local pattern detector is built by the weighted combination of these selected weak detectors.Finally,vehicle detection can be performed in still images by using the boosted strong local feature detector.Experiment results show that the local pattern detectur constructed in this way combines the advantages of Haar-like and oriented gradient features,and can achieve better detection results than the datector by using single Haar-like features.