LAUSR

The increasing use of rotary-wing UAVs poses security risks, which makes image detection of rotary-wing UAVs a critical issue. This paper proposes an object detection algorithm for rotary-wing UAVs based on a transformer network. A self-attention mechanism is used to utilize the local contextual information to extract the features of the rotary-wing UAV more effectively, which improves the accuracy of object detection. Meanwhile, a new self-attention mechanism is designed, in which the query vector and the key vector of the surrounding annular area are calculated separately and then concatenated by different heads of attention. Experimental results show that, compared with existing algorithms, the proposed algorithm improves the mean average precision by 1.7% on the proposed rotary-wing UAV dataset.