Unmanned Aerial Vehicles (UAVs) have been developed in the last decades to meet some of the demands that typical military aircraft, and their human crews, could not carry out safely and effectively. A human crew cannot fly longer than a UAV in many dangerous situations that could jeopardize the safety of the crew. With their relatively small size, not much fuel can be carried on-board to carry out an extended mission. If a UAV is to be successful, it must be able to refuel in-air to carry out more extended missions. Some of the most difficult challenges a human pilot must face are the probe-and-drogue aerial refueling problem that requires great skill to dock an aircraft to another aircraft to refuel. This thesis develops a Deep Learning Object Detector to provide accurate 6-DoF information of the drogue relative to a monocular camera that is onbaord of a flying UAV. An object detector will help provide the needed information for an autonomous vehicle to dock and refuel without the need for human intervention. The object detector was trained using 8746 images of a mock drogue to detect eight different beacons. Once these beacons were detected, a non-linear least-squares algorithm that uses the collinearity equations as a system model takes the location of the beacons on the captured image to provide an accurate 6-DoF navigation solution. These navigation solutions from the Object Detector were evaluated on multiple metrics and then compared to navigation solutions provided by a VICON motion tracking system. Finally, Monte Carlo analysis was performed using the collinearity equations as a system model to evaluate the performance of an Object Detector with various degrees of noise.