摘要
针对钢卷端面边损和松卷表面缺陷检测困难的问题,提出了一种基于像素纹理特征的钢卷端面缺陷检测方法。首先,结合灰度共生矩阵(Gray-Level Co-occurrence Matrix,GLCM)与高斯滤波得到图像的预处理数据,方便梯度算法提取其中的轮廓信息;随后,基于轮廓信息中提取的矩阵旋转中心计算得到旋转矩阵,以利用仿射变换使钢卷带的方向迭代旋转至垂直;然后,利用CUSUM(Cumulative Sum)统计算法区分PCA(Principal Components Analysis)算法统计得到的纵向像素值中的高亮和高暗区域,以区分边损缺陷和松卷缺陷;最后,计算图像数据中的波峰及波谷值,实现对任意纹理方向2类缺陷的自动检测和精确定位。实验结果表明,边损缺陷检测的精确率、召回率和平均精度均值分别达到96.2%、95.6%和96.4%,松卷缺陷检测的精确率、召回率和平均精度均值分别达到98.0%、96.9%和99.2%;总体精确率达到97.7%,召回率达到96.8%,平均精度均值达到97.3%。实验结果证明了所提方法在检测边损缺陷和松卷缺陷上具有较高的准确性和较好的可靠性。所提方法为工业生产中钢卷端面缺陷自动检测提供了一种思路。
To address the challenges in detecting edge damage and loose coil surface defects in steel coils end-face,a steel coil end-face defect detection method based on pixel texture features was proposed.First,Gray-Level Co-occurrence Matrix(GLCM)and Gaussian filtering were combined to preprocess the images.This facilitated the extraction of contour information using gradient algorithms.Next,rotation centers were calculated from the extracted contour matrices.Rotation matrices were generated to iteratively align the steel coil strips to a vertical orientation using affine transformation.Then,the CUSUM statistical algorithm was applied to distinguish high-brightness and high-darkness regions in longitudinal pixel values obtained from PCA-based statistics.This allowed the differentiation between edge damage defects and loose coil defects.Finally,the peak and valley values in the image data were calculated.This enabled the automated detection and precise localization of the two types of defects in arbitrary texture orientations.The experimental results showed that the precision,recall,and mean Average Precision(mAP)for edge damage defect detection reached 96.2%,95.6%,and 96.4%,respectively.For loose coil defect detection,these metrics were 98.0%,96.9%,and 99.2%,respectively.The overall precision,recall,and mAP achieved 97.7%,96.8%,and 97.3%,respectively.The results demonstrated that the proposed method achieved high accuracy and reliability in detecting edge damage and loose coil defects.This method provided a potential solution for automated detection of steel coil end-face defects in industrial production.
作者
汪劲雯
许新科
赵龙彪
袁狄剑
杜昱
WANG Jinwen;XU Xinke;ZHAO Longbiao;YUAN Dijian;DU Yu(College of Metrology Measurement and Instrument,China Jiliang University,Hangzhou 310018,China;292228 troops of the Chinese People′s Liberation Army,Beijing 100072,China)
出处
《现代制造工程》
北大核心
2025年第12期105-113,129,共10页
Modern Manufacturing Engineering
基金
浙江省自然科学基金项目(LY19F050008)。
关键词
钢卷端面
表面缺陷检测
灰度共生矩阵
仿射变换
CUSUM统计算法
steel coil end-face
surface defect detection
Gray-Level Co-occurrence Matrix(GLCM)
affine transformation
CUSUM statistical algorithm
