Abstract
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.
Highlights
With the development of science and technology, the application of rotary-wing drones is increasing
A transformer network based on the annular window (AWin Transformer) is designed to improve the multi-head self-attention mechanism based on the sliding window
As the number of stages in the transformer network increases with the resolution of the feature map, the value of rwmax can be increased appropriately to meet the receptive field of the object
Summary
With the development of science and technology, the application of rotary-wing drones is increasing. The analysis of the existing methods reveals that the front and back scenes are confused during UAV detection, resulting in false alarms and missing alarms This is because the extraction of contextual information is not sufficient in the current studies, and there is no more effective self-attention mechanism designed for rotary-wing UAVs. To solve the problem of missed and false alarms in the detection of rotary-wing UAVs and to improve the accuracy of object detection, this paper proposes a UAV object detection algorithm based on an annular window transformer network, which has a good learning ability for context connection. By combining the contextual connection of deep self-attention transformers and the hierarchical and progressive detection characteristics of convolutional networks, this algorithm achieves high-precision rotary-wing UAV object detection.
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