UAV Formation Leader Detection Algorithm Based on YOLOv8
The vision-based unmanned aerial vehicle(UAV)formation method has the advantage of being unaffected by communication disruptions and exhibits greater robustness compared to traditional for-mation algorithms,gradually becoming a research hotspot in the field.In the Leader-Follower UAV visual formation mode,followers achieve formation control by performing real-time target detection on the lead-er and calculating the relative positional relationship between the leader and the followers.This paper pro-poses an improved real-time object detection algorithm based on the YOLOv8n object detection model:convolution modules were added in the Neck module,a multi-head attention mechanism was added to en-hance feature extraction,apply data augmentation was applied in the training process.To validate the per-formance advantages of the algorithm proposed,two comparative tests were conducted.The experimental results indicate that the improved algorithm exhibits stronger feature extraction and higher detection accu-racy compared to the original algorithm.Finally,the improved object detection algorithm is applied to drone formation tasks,demonstrating the practical utility of the algorithm in this context.
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