Swarm Reconnaissance Drone System for Real-Time Object Detection Over a Large Area

Recent developments in drone technology have led to the widespread use of unmanned aerial vehicles (UAVs). In particular, UAVs are often used in reconnaissance to detect objects such as missing persons in large areas. However, traditional systems use only one UAV to search for missing persons in a large area. In addition, object detection is performed after flight or manually because detection requires high computing power. In this paper, a reconnaissance drone system using multiple UAVs is proposed. The proposed multi-UAV reconnaissance system performs real-time object detection on each UAV. The real-time object detection results from each UAV are received by the ground control system (GCS) to stitch the images. To enable real-time object detection in individual UAVs, the filter pruning method is applied to the YOLOv5 model, and the model uses 40% fewer parameters than the existing baseline model. The lightweight YOLOv5 model achieves approximately 11.73 FPS on the Jetson Xaiver NX using a mission computer. Moreover, the proposed image stitching method enables image stitching by effectively matching features using additional information generated by UAVs. The UAV flight tests show that the proposed reconnaissance system can monitor and detect objects in real time over large areas.

Automated summary

Recent developments in drone technology have led to the widespread use of unmanned aerial vehicles (UAVs). In particular, UAVs are often used in reconnaissance to detect objects such as missing persons in large areas. However, traditional systems use only one UAV to search for missing persons in a large area. This paper proposes a reconnaissance drone system using multiple UAVs, which performs real-time object detection on each UAV and stitches the images together using a ground control system (GCS). The lightweight YOLOv5 model and the image stitching method effectively enable real-time monitoring and detection of objects over large areas.