| Summary: | A drone is a light unmanned aerial vehicle capable of precise and agile movement. As these traits and the usability of drones are recognized in more domains, the necessity to ensure a safe airspace increases. To minimize the risk of airborne collision, this paper aims to investigate the feasibility of real-time object detectionusing convolutional neural networks to detect aircrafts from distances over 1000 meters. Early detection of aircrafts increases the drone operator's overall time for avoidance, however make aircrafts display little to no distinguishing features, making object detection difficult. To test its applicability, the object detection model is incorporated in a sense-and-warn system to provide an end- to-end solution, requiring only average computational capabilities and a drone with a monocular camera. Results generated from a virtual environment show that detections far exceed the target of 1000 meters and is able to efficiently detect, track and estimate collisions of airborne aircrafts. Compared to a human observer, the proposed system is able to detect object at approximately twice the distance.
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