This study presented a comprehensive investigation into the performance of object detection models tailored for edge devices, particularly in the context of Unmanned Aerial Vehicle swarms. Object detection plays a pivotal role in enhancing autonomous navigation, situational awareness, and target tracking capabilities within UAV swarms, where computing resources are constrained by the onboard low-cost computers. Initially, a thorough review of the existing literature was conducted to identify state-of-the-art object detection models suitable for deployment on edge devices. These models are evaluated based on their speed, accuracy, and efficiency, with a focus on real-time inference capabilities crucial for Unmanned Aerial Vehicle applications. Following the literature review, selected models undergo empirical validation through custom training using the Vision Meets Drone dataset, a widely recognized dataset for Unmanned Aerial Vehicle-based object detection tasks. Performance metrics such as mean average precision, inference speed, and resource utilization were measured and compared across different models. Lastly, the study extended its analysis beyond traditional object detection to explore the efficacy of instance segmentation and proposed an optimization to an object tracking technique within the context of unmanned Aerial Vehicles. Instance segmentation offers finer-grained object delineation, enabling more precise target or landmark identification and tracking, albeit at higher resource usage and higher inference time.

Multiple-object detection, localization, and tracking are desirable in many areas and applications, as the field of deep learning has developed and has drawn the attention of academics in computer vision, having a plethora of networks now achieving excellent accuracy in detecting multiple objects in an image. Tracking and localizing objects still remain difficult processes which require significant effort. This work describes an optical camera-based target detection, tracking, and localization solution for Unmanned Aerial Vehicles (UAVs). Based on the well-known network YOLOv4, a custom object detection model was developed and its performance was compared to YOLOv4-Tiny, YOLOv4-608, and YOLOv7-Tiny. The target tracking algorithm we use is based on Deep SORT, providing cutting-edge tracking. The proposed localization approach can accurately determine the position of ground targets identified by the custom object detection model. Moreover, an implementation of a global tracker using localization information from up to four UAV cameras at a time. Finally, a guiding approach is described, which is responsible for providing real-time movement commands for the UAV to follow and cover a designated target. The complete system was evaluated in Gazebo with up to four UAVs utilizing Software-In-The-Loop (SITL) simulation.

A decentralized swarm of quadcopters designed for monitoring an open area and detecting intruders is proposed. The system is designed to be scalable and robust. The most important aspect of the system is the swarm intelligent decision-making process that was developed. The rest of the algorithms essential for the system to be completed are also described. The designed algorithms were developed using ROS and tested with SITL simulations in the GAZEBO environment. The proposed approach was tested against two other similar surveilling swarms and one approach using static cameras. The addition of the real-time decision-making capability offers the swarm a clear advantage over similar systems, as depicted in the simulation results.

The multiple advantages of the use of Unmanned Aerial Vehicle (UAV) platforms in combination with the recent research advancements in the field and the reduction of production cost for such platforms have caused an increasing interest for their use in multiple civilian applications. As the number of UAV operations in a common airspace increases, Unmanned Traffic Management (UTM) plays a crucial role in accommodating flights in a safe and systematic manner. This work looks into six categories of those applications and the relevant guidance and decision-making algorithms and methodologies to enable autonomy in their execution, as well as traffic management systems required to support them.

The numerous advantages of using UAV platforms, alongside with recent scientific developments in the field of autonomous vehicles in general and the lower production costs for such platforms, have increased interest in their usage in a variety of defense applications. This work investigates swarming in defense applications and provides information about the crucial modules needed for a swarm to operate and the main missions in defense applications that the swarms can be used to enhance the situational awareness.