Discrimination of pores and cracks in iron ore pellets using deep learning neural networks

Abstract The discrimination between pores and cracks is an important step in the microstructural analysis of iron ore pellets. While the porosity is fundamental during the reduction process in blast furnaces, cracks are strongly detrimental to the mechanical strength. The usual image processing tools cannot automatically discriminate between these two types of features, especially in 3D images obtained, for instance, with x-ray microtomography (microCT). As pores and cracks have essentially the same x-ray absorbance, they cannot be discriminated by a simple intensity threshold. Given the compl...