【作者】 王丹；

【导师】 高炜欣；

【作者基本信息】 西安石油大学 ， 电气工程（专业学位）， 2023， 硕士

【摘要】 管道安全是能源行业可靠运行的基础,随着我国环保力度的加大,越来越多的城市热电厂实现煤改气的同时管道建设也迎来了大的发展时期。焊接质量是影响管道安全的重要因素,在各种焊接质量检测方法中,X射线检测是使用最为广泛的技术,传统人工评片模式不仅工作量大,检测结果受检测人员主观因素影响,易发生漏检。为提高检测准确率,利用图像处理技术进行自动检测已经成为当前研究的热点。文章以管道X射线环焊缝图像为研究对象,针对不同类型缺陷,提出利用稀疏描述技术和深度卷积网络进行检测。为提高计算速度,满足现场实际需求,引入并行计算,提高检测的实时性和准确性。论文主要从以下5个方面进行了研究:(1)针对X射线环焊缝图像噪声多、对比度低、难以直接处理的问题,首先采用中值滤波法降噪,结合伽马变换增强,提高图像清晰度。然后利用最大类间方差法与Sobel算子相结合,准确提取感兴趣区域,最后运用密度聚类算法,将疑似缺陷区域进行精准分割。(2)针对裂纹和以柱针为代表的圆形缺陷等相对面积较小、常规方法难以识别的问题,提出将稀疏描述技术与字典学习技术相结合,利用基于期望投影的字典模型构建算法,生成小缺陷字典矩阵。识别时,利用稀疏求解技术拟合待检测图像,通过稀疏系数来判定疑似图像是否为缺陷。(3)针对内凹、未焊透以及未熔合等面积较大缺陷的识别问题,提出将Inception模块与Res Net网络相结合,设计了适用于识别面积较大型缺陷的深度卷积神经网络模型。所建模型可以较好地提取图像深层特征,训练时采用交叉熵损失函数和自适应矩估计算法优化网络参数。实验结果表明,该模型具有很好的鲁棒性和较高的缺陷识别率。(4)为了提高缺陷检测的实时性,采用基于CUDA平台的GPU并行加速技术,对图像预处理模块进行了优化,提高了图像的处理速度。此外,运用CPU多核多线程的功能,实现两种识别算法的并行运行,促进了系统的高效化。(5)根据实际工业检测需求,研发了焊缝缺陷智能识别系统并通过了SGS通标标准技术服务有限公司的目击测试。该系统可在无人工干预的前提下对缺陷进行准确检出,检出类型覆盖全面,检出速率快,识别准确率高,可有效缓解检测人员工作压力。更多还原

【Abstract】 Pipeline safety is the basis of safe and reliable operation of energy industry.With the increase of environmental protection in our country,more and more urban thermal power plants are replacing coal with gas,and pipeline construction is ushered in a great development period.Welding quality is an important factor affecting pipeline safety.Among all kinds of welding quality testing methods,X-ray testing is the most widely used technology.However,the traditional manual film evaluation mode not only requires a lot of work,but also the detection results are affected by subjective factors of the detection personnel.In order to improve the detection accuracy,automatic detection using image processing technology has become a hot topic in current research.In this paper,the X-ray girth weld image of pipeline was taken as the research object,and sparse description technique and deep convolutional network were proposed to detect different types of defects.In order to improve the computing speed and meet the actual needs of the field,parallel computing is introduced to improve the real-time and accuracy of detection.This paper mainly studies from the following five aspects:(1)In view of the problems of excessive noise,low contrast and difficult direct processing of X-ray weld images,this paper firstly adopts the median filtering method for noise reduction,combined with gamma transform enhancement,to improve the image clarity.Then,the maximum inter-class variance method is combined with the Sobel operator to extract the region of interest accurately.Finally,the density clustering algorithm is used to segment the suspected defect region accurately.(2)Aiming at the problems of small relative area such as cracks and circular defects represented by pins and needles,which are difficult to be identified by conventional methods,the sparse description technique is combined with dictionary learning technique,and the dictionary model construction algorithm based on expectation projection is used to construct the dictionary matrix of small defects.In recognition,sparse solving technology is used to fit the image to be detected,and the sparse coefficient is used to determine whether the suspected image is a defect.(3)Aiming at the identification problem of defects with large area such as concave,underwelded and unfused,the Inception module was combined with Res Net network to design a deep convolutional neural network model suitable for the identification of defects with large area.The model can extract the deep features of the image well,and the network parameters are optimized by using the cross entropy loss function and the adaptive moment estimation algorithm.Experimental results show that the model has good robustness and high defect recognition rate.(4)In order to improve the real-time performance of defect detection,this paper adopts GPU parallel acceleration technology based on CUDA platform to optimize the image preprocessing module and improve the image processing speed.In addition,the parallel running of the two recognition algorithms is realized by using the function of CPU multi-core and multi-threading,which promotes the high efficiency of the system.(5)According to the actual industrial testing requirements,this paper developed an intelligent identification system for weld defects and passed the eyewitness test of SGS-CSTC Standard Technical Services Co.,LTD.The system can accurately detect defects without manual intervention,with comprehensive coverage of detection types,fast detection rate and high recognition accuracy,which effectively relieves the working pressure of detection personnel.更多还原