Multi-Type Target Detection and Tracking in UAV Images under Typical Scenarios

Despite significant advances in UAV target detection and tracking algorithms, real-world flight tests and data validation remain insufficient. In this study, we developed a stable and reliable UAV image target detection and tracking system based on the YOLOv5 and TCTrack networks. The system demonstrated excellent stability and reliability in laboratory-simulated scenarios, particularly excelling in target recognition accuracy. Additionally, the system was tested in real UAV flight scenarios, showcasing robust real-time performance suitable for various task scenarios. Furthermore, we collected and annotated a comprehensive outdoor multi-scene target detection dataset by operating different types of UAVs to further validate and optimize the algorithm.