Automated airborne collision-detection systems are a key enabling technology for facilitating the integration of unmanned aerial vehicles (UAVs) into the national airspace. These safety-critical systems must be sensitive enough to provide timely warnings of genuine airborne collision threats, but not so sensitive as to cause excessive false alarms. Hence, an accurate characterization of detection and false-alarm sensitivity is essential for understanding performance tradeoffs, and system designers can exploit this characterization to help achieve a desired balance in system performance. In this paper, we experimentally evaluate a sky-region, image-based, aircraft collision-detection system that is based on morphological and temporal processing techniques. (Note that the examined detection approaches are not suitable for the detection of potential collision threats against a ground clutter background.) A novel collection methodology for collecting realistic airborne collision-course target footage in both head-on and tail-chase engagement geometries is described. Under (hazy) blue sky conditions, our proposed system achieved detection ranges greater than 1540 m in three flight test cases with no false-alarm events in 14.14 h of nontarget data (under cloudy conditions, the system achieved detection ranges greater than 1170 m in four flight test cases with no false-alarm events in 6.63 h of nontarget data). Importantly, this paper is the first documented presentation of detection range versus false-alarm curves generated from airborne target and nontarget image data. © 2012 Wiley Periodicals, Inc.