  With the rapid development of telescopes, both temporal cadence and the
spatial resolution of observations are increasing. This in turn generates vast
amount of data, which can be efficiently searched only with automated
detections in order to derive the features of interest in the observations. A
number of automated detection methods and algorithms have been developed for
solar activities, based on the image processing and machine learning
techniques. In this paper, after briefly reviewing some automated detection
methods, we describe our efficient and versatile automated detection method for
solar filaments. It is able not only to recognize filaments, determine the
features such as the position, area, spine, and other relevant parameters, but
also to trace the daily evolution of the filaments. It is applied to process
the full disk H-alpha data observed in nearly three solar cycles, and some
statistic results are presented.
