  Wide field small aperture telescopes (WFSATs) are commonly used for fast sky
survey. Telescope arrays composed by several WFSATs are capable to scan sky
several times per night. Huge amount of data would be obtained by them and
these data need to be processed immediately. In this paper, we propose ARGUS
(Astronomical taRGets detection framework for Unified telescopes) for real-time
transit detection. The ARGUS uses a deep learning based astronomical detection
algorithm implemented in embedded devices in each WFSATs to detect astronomical
targets. The position and probability of a detection being an astronomical
targets will be sent to a trained ensemble learning algorithm to output
information of celestial sources. After matching these sources with star
catalog, ARGUS will directly output type and positions of transient candidates.
We use simulated data to test the performance of ARGUS and find that ARGUS can
increase the performance of WFSATs in transient detection tasks robustly.
