Under most microscopes, the fluorescent signals emitted from biological structures, such as vesicles, as well as the signals from single molecules will appear as small puncta, which contribute to a Gaussian-like distribution. Accurate segmentation of these spots will fundamentally affect our interpretation of a specific biological progress. Because of the complicated backgrounds in images, many algorithms fail to identify all of the interesting signals; the tremendous amount of time required for algorithms to process large datasets can also decrease their utility. Here, we introduce an excellent robust detection method based on the machine learning algorithm AdaBoost, which outperforms threshold-based segmentation,wavelets, and FDA under most situations. We also provide a GPU/multi-core CPU implementation of this algorithm;this implementation accelerates the algorithm approximately 10- and 7-fold acceleration compared with a single CPU implementation. The great reduction of time should make this method a promising candidate in the processing of large datasets. Furthermore, we demonstrate the use of our algorithm on true fluorescent micrographs, and the results show that machine learning-based detection methods outperform the four other previously reported methods. Under most microscopes, the fluorescent signals emitted from biological structures, such as vesicles, as well as the signals from single molecules will appear as small puncta, which contribute to a Gaussian-like distribution. Accurate segmentation of these spots will fundamentally affect our interpretation of a specific biological progress. Because of the complicated backgrounds in images, many algorithms fail to identify all of the interesting signals; the tremendous amount of time required for algorithms to process large datasets can also decrease their utility. Here, we introduce an excellent robust detection method based on the machine learning algorithm AdaBoost, which outperforms threshold-based segmentation, wavelets, and FDA under the most situations. We also provide a GPU / multi-core CPU implementation of this algorithm; this implementation accelerates the algorithm approximately 10- and 7- fold acceleration compared with a single CPU implementation. The great reduction of time should make this method a promising candidate in the processing of large datasets. and demonstrate the use of our algorithm on true fluorescent micrographs, and the results show that machine learning-based detection methods outperform the four previously previously reported methods.