There are diverse types of defects with different forms, including scale, intensity, shape, and so on. It is a challenging task to detect all types of defects equally only with the same method. In this paper, a novel fuzzy measure-based method is presented to detect the defects on strip steel surfaces. By the statistical information of strip steel defect-free images, it is assumed that the background intensity of strip steel image obeys Gaussian distribution. Firstly, the histogram of a given test image is created, from which the model parameters of background are estimated. Then, a membership function is defined to estimate the extent to which each gray level belongs to defect, by which each pixel of test image obtains a membership value. So far, most of pixels can be detected as defect or background. Finally, combining the pixel connectivity, the maximum and sum of fuzzy connected regions are used to locate defects. Experimental results show that the proposed method achieves a high detection rate of 96.8%.

缺陷的类型多种多样，包括大小，强度，形状等。仅使用同一方法平等地检测所有类型的缺陷是一项艰巨的任务。本文提出了一种基于模糊测度的新颖方法来检测带钢表面的缺陷。根据带钢无缺陷图像的统计信息，可以认为带钢图像的背景强度服从高斯分布。首先，创建给定测试图像的直方图，从中估计背景的模型参数。然后，定义隶属度函数以估计每个灰度级属于缺陷的程度，通过该隶属度函数，测试图像的每个像素将获得隶属度值。到目前为止，大多数像素都可以检测为缺陷或背景。最后，结合像素连通性，模糊连接区域的最大值和总和用于定位缺陷。实验结果表明，该方法检测率高达96.8％。