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This paper showcases the integration of several technologies to develop an Unmanned Traffic Management System that enables the centralized coordination of unmanned ground and aerial vehicles. By addressing the need for safe and efficient autonomous vehicle operations, this system contributes to improved safety and reliability in various applications, from civilian to military contexts. Furthermore, the exploration of dynamic vision-based drone detection methods adds valuable insights into the field of real-time image processing and deep learning. In that perspective, a more in-depth computer vision development is been presented. The system's core components include the Swarmie, an unmanned ground vehicle (UGV) guided through a wireless mesh network through radio frequency enabled (RF) markers. Simultaneously, an unmanned aircraft vehicle (UAV) is controlled by an IoT cloud platform that sends coordinates to an embedded system. The integration of wireless communication and navigation markers is a proof to the importance of circuitry and microcontrollers in developing RF markers to enhance navigation. One of the primary objectives of this research is the development of a dynamic vision-based drone detection system for sense and avoid actions. Two different methods are explored for drone detection. The first method utilizes the Viola & Jones algorithm. The second method involves the You Only Look Once (YOLO) RealTime Object Detection algorithm. The performance of these methods is evaluated, providing insights into the effectiveness of each approach in real-time drone detection.
Rangel et al. (Fri,) studied this question.