一种基于Sobel算子的带钢表面缺陷图像边缘检测算法  

An image edge detection algorithm for strip steel surface defects based on Sobel operator

作　　者：刘源[1] 夏春蕾[1] Liu Yuan;Xia Chunlei(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《电子测量技术》2021年第3期138-143,共6页Electronic Measurement Technology

摘　　要：基于机器视觉的带钢表面缺陷检测是带钢轧制过程中重要的质量检测方法,为了提高带钢表面缺陷检测的效率和准确率,提出了一种新的带钢表面缺陷图像边缘检测算法。该算法首先用双边滤波去除图像噪声,达到保边去噪的目的,然后用改进的四方向Sobel算子检测缺陷图像边缘,并用自适应动态阈值选取最佳阈值进行二值化处理,最后将二值化图像进行基于Hilditch算法的边缘细化处理,得到最终检测图像。在MATLAB平台上对算法进行仿真,并将得到的实验结果与传统Sobel算子进行比较。实验结果表明,所提算法平均分割正确率达到93.5%,与传统Sobel算子边缘检测方法相比,所提算法能得到更好的边缘检测效果。Strip steel surface defect detection based on machine vision is an important quality inspection method in the strip rolling process.In order to improve the efficiency and accuracy of strip surface defect detection, this paper proposes a new strip steel surface defect image edge detection algorithm.The algorithm first uses bilateral filtering to remove image noise to achieve the purpose of edge preservation and denoising, then uses an improved four-direction Sobel operator to detect the edges of defective images, and uses adaptive dynamic thresholds to select the best threshold for binarization.The valued image is processed by edge thinning based on Hilditch algorithm to obtain the final detection image.The algorithm is simulated on the MATLAB platform, and the experimental results obtained are compared with the traditional Sobel operator. Experimental results show that the average segmentation accuracy of the algorithm reaches 93.5%. Compared with the traditional Sobel operator edge detection method, the algorithm can obtain better edge detection results.

关 键 词：双边滤波 SOBEL算子 图像细化 边缘检测 带钢表面缺陷 

分 类 号：TN37[电子电信—物理电子学]