Abstract
To address the poor accuracy issue with tiny target recognition by UAVs, this study provides an improved YOLOv5 detection method with an attention mechanism. Firstly, CBAM is integrated into Backbone to suppress irrelevant features and enhance the network’s attention to space and channels. This can help the network learn more discriminative representations of objects in the image. Then, the introduction to Biformer in Neck removes redundant information on the algorithm structure, endows the network with dynamic query-aware sparsity, and enhances its ability to detect small targets. The experimental findings demonstrate that the suggested algorithm model has a detection accuracy of 84.6% on average. in the self-built UAV dataset, and can accurately complete the detection task of small UAV targets.
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