With the deployment of large CCD mosaic cameras and their use in large-scale surveys to discover Solar System objects, there is a need for a fast detection algorithms that can handle large data loads in a nearly automatic way. We present here an algorithm that we have developed. Our approach, by using two independent detection algorithms and combining the results, maintains high efficiency while producing low false detection rates. These properties are crucial to in order to reduce the operator time associated with searching these huge data sets. We have used this algorithm on two different mosaic data sets obtained using the CFH12K camera at CFHT. Comparing the detection efficiency and false-detection rate of each individual algorithm with the combination of both, we show that our approach decreases the false detection rate by a factor of a few hundred to a thousand, while decreasing the %BG added qualifier. potentially eliminate 'limiting magnitude' entirely). `limiting magnitude' (where the detection rate drops to 50\%) by only 0.1-0.3 magnitudes. The limiting magnitude is similar to that of a human operator blinking the images. Our full pipeline also characterizes the magnitude efficiency of the entire system by implanting artificial objects in the data set. The detection portion of the package is publicly available.