摘要
管道作为工业、核设施、石油天然气等领域中常用的物料输送手段,在使用过程中极易出现各类缺陷,传统的人工检测存在准确率低、效率低、成本高等缺点,采用数字图像处理技术可以对管道图像进行自动检测与分类,有效克服上述缺点。首先使用图像增强、图像分割、数学形态学以及边界跟踪对图像进行预处理,在提取出缺陷区域的尺寸、形状和纹理特征后,选择圆形度、凸度、离心率、熵、相关性和聚集度作为模式识别的特征向量,最后综合使用基于粒子群优化的K-means聚类分析和统计模式识别分类器进行分类。使用文中的图像预处理算法可以成功的将管道缺陷提取出来,达到管道缺陷自动检测的目的。基于粒子群优化的K-means聚类分析成功的将管道缺陷图像归为裂纹缺陷、管接头缺陷和孔形腐蚀三类,相比于传统K-means算法,聚类准确率分别提高9%、16.7%、12.5%。综合使用基于粒子群优化的K-means聚类分析和统计模式识别分类器对管道缺陷进行分类,三类缺陷的分类准确率均在80%以上,其中管接头缺陷和孔形腐蚀的准确率达到90%以上。综上,综合集成出了一套基于数字图像处理技术的管道缺陷自动检测与分类算法方案,实验结果表明,该算法方案具有自动化程度高、通用性强、准确率高的特点。
In the fields of industry, nuclear facilities, oil and gas, pipe is commonly used as the means of material delivery. And it is easy to appear various defects. The traditional manual detection system has the disadvantages of low accuracy, low efficiency and high cost. The digital image processing technology can automatically detect and classify the pipe image, thus effectively overcoming the above shortcomings. First, image enhancement, image segmentation, mathematical morphology and boundary tracking are used for image preprocessing. Then, after extracting the size, shape and texture features of the defective area, we choose the circularity, convexity, eccentricity, entropy, correlation and cluster tendency as the feature vector. Finally, K-means clustering analysis based on particle swarm optimization and statistical pattern recognition classifier is used for classification. Using the image preprocessing algorithm in this paper, we can successfully extract the pipe defects and achieve the purpose of automated pipe defect detection. K-means clustering analysis based on particle swarm optimization successfully clusters the pipe defect images into three categories which are crack defects, pipe joint defects and hole corrosion respectively. Compared with the traditional K-means algorithm,K-means clustering analysis based on particle swarm optimization can increase clustering accuracy by 9%, 16.7% and 12.5% respectively. The clustering analysis based on particle swarm optimization and the statistical pattern recognition classifier is used to classify the pipe defects. The classification accuracy of the three types of defects is more than 80%. The accuracy of pipe joint defects and hole corrosion is more than 90%. In summary, an integrated algorithm scheme for automated pipe defect detection and classification based on digital image processing technology is proposed. The experiments show that the algorithm scheme has the characteristics of high degree of automation, high versatility and accuracy.
出处
《图学学报》
CSCD
北大核心
2017年第6期851-856,共6页
Journal of Graphics
关键词
管道缺陷检测
图像处理
粒子群优化
聚类分析
统计模式识别
pipe defect detection
image processing
particle swarm optimization
cluster analysis
statistical pattern recognition
