Indentation Image Analysis for Vickers Hardness Testing

The paper presents a novel algorithm for detection and measurement of indentations in Vickers hardness testing images, within a specific case of applied research on material quality evaluation based on image processing. The algorithm performs image segmentation by binarization, morphological filtering, and region growing, where the binarization threshold is automatically obtained from the input image. After identification of the rhombus shaped indentation footprint, its four vertices are determined using corner detection, which are used to calculate the diagonal lengths and the Vickers hardness number. The proposed procedure has been tested on 185 images of real data obtained by the micro hardness machine Mitutoyo HM-124, mostly from Steel-316 specimens, but also from Hafnium Nitride. Test images include specular-polished and rough surfaces, specimen with artifacts or imperfections, indentations with deformed or damaged edges, and low contrast images. Ground true diagonal lengths obtained in the conventional manual manner by an expert were compared with the results determined by our method. The proposed method achieves competitive accuracy compared to the best known methods, but it is simpler and hence more efficient.