Abstract
The wide application of UAV technology in various fields makes UAV target detection crucial. In this study, we propose an improved algorithm based on YOLOv7 to enhance the performance and robustness of UAV target detection. We utilize YOLOv7 as the infrastructure and introduce BiFPN (Bi-directional Feature Pyramid Network) to enhance the feature fusion, while adding the GAM attention mechanism to the model, which is trained and evaluated using the VisDrone2019 dataset. The experimental results of this study show that the improved model achieves an average accuracy mAP value of 45.6%, which is 2.7% higher than the traditional model, and is able to detect and localize UAV targets more accurately.
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