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In live-cell fluorescence microscopy imaging, quantitative analysis of biological image data generally involves the detection of many subresolution objects, appearing as diffraction-limited spots. Due to acquisition limitations, the signal-to-noise ratio (SNR) can be extremely low, making automated spot detection a very challenging task. In this paper, we quantitatively evaluate the performance of the most frequently used supervised and unsupervised detection methods for this purpose. Experiments on synthetic images of three different types, for which ground truth was available, as well as on real image data sets acquired for two different biological studies, for which we obtained expert manual annotations for comparison, revealed that for very low SNRs (ap2), the supervised (machine learning) methods perform best overall, closely followed by the detectors based on the so-called h-dome transform from mathematical morphology and the multiscale variance-stabilizing transform, which do not require a learning stage. At high SNRs (>5), the difference in performance of all considered detectors becomes negligible.