Machine vision represents a particularly attractive solution for sensing and detecting potential collision-course targets due to the relatively low cost, size, weight, and power requirements of vision sensors (as opposed to radar and Traffic Alert and Collision Avoidance System). This paper describes the development and evaluation of a real-time, vision-based collision-detection system suitable for fixed-wing aerial robotics. Using two fixedwing unmanned aerial vehicles (UAVs) to recreate various collision-course scenarios, we were able to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. This type of image data is extremely scarce and was invaluable in evaluating the detection performance of two candidate target detection approaches. Based on the collected data, our detection approaches were able to detect targets at distances ranging from 400 to about 900 m. These distances (with some assumptions about closing speeds and aircraft trajectories) translate to an advance warning of between 8 and 10 s ahead of impact, which approaches the 12.5-s response time recommended for human pilots. We overcame the challenge of achieving real-time computational speeds by exploiting the parallel processing architectures of graphics processing units (GPUs) found on commercial-off-the-shelf graphics devices. Our chosen GPU device suitable for integration onto UAV platforms can be expected to handle real-time processing of 1,024 × 768 pixel image frames at a rate of approximately 30 Hz. Flight trials using manned Cessna aircraft in which all processing is performed onboard will be conducted in the near future, followed by further experiments with fully autonomous UAV platforms. C

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.

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