In this paper, we present a novel algorithm for the automatic detection and measurement of Vickers indentation hardness, using image processing. This algorithm uses image segmentation via binarization, automatically evaluating the mean and extreme gray values by means of standard histogram equalization so as to determine the optimal binarization threshold from each input image. We use a morphological filter and region growing to identify the indentation footprint. Our algorithm determines the four indentation vertices required to calculate diagonal lengths and Vickers hardness number. This algorithm is applied to 230 indentation images of steel-316 and hafnium nitride specimens, obtained using a micro hardness machine. The proposed algorithm can measure the Vickers hardness number of specimens using their indentation images. The algorithm results have a relative error of less than 3% with respect to those obtained through a conventional manual procedure. This algorithm can be used for indentation images with low contrast and irregular indentation edges.

在本文中，我们提出了一种使用图像处理自动检测和测量维氏压痕硬度的新算法。该算法使用通过二值化的图像分割，通过标准直方图均衡化自动评估均值和极值灰度值，以便从每个输入图像中确定最佳二值化阈值。我们使用形态过滤器和区域生长来识别压痕足迹。我们的算法确定了计算对角线长度和维氏硬度值所需的四个压痕顶点。该算法适用于使用显微硬度机获得的230毫米的316钢和氮化ha试样的压痕图像。所提出的算法可以使用压痕图像来测量样品的维氏硬度值。相对于通过常规手动程序获得的结果，算法结果的相对误差小于3％。该算法可用于低对比度和不规则压痕边缘的压痕图像。