We compared three commonly used algorithms for detecting fiducial markers in electron microscopy images . The algorithms were implemented in a unified codebase in the software package Ettention  to ensure comparability of results without the influence of software. Several evaluation metrics were introduced to assess the capabilities of the algorithms on basis of four datasets. We showed, that depending on a dataset, different algorithms performed best. This proved, that the choice of a marker detection algorithm highly depends on the properties of a dataset to be analyzed, which makes it difficult to achieve best possible marker detection capabilities on a wide range of datasets with varying properties. Hence, more sophisticated marker detection methods may be needed to ensure the proper working of subsequent steps like alignment.