UAV reconnaissance has become an important means of battlefield reconnaissance and has received much attention in the military development of various countries. Therefore, UAV target detection accuracy is an important factor that restricts the effectiveness of military reconnaissance. How to improve the efficiency of UAV target detection and design high-precision neural network algorithms is the main direction of research in this paper. In this paper, for the current UAV target detection accuracy is not high, the FPG-based Transformer algorithm is proposed, which is mainly based on the Transformer, the Transformer Block is improved, and at the same time, the FPG pyramid structure is introduced in the downstream task, and the results show that, after the improvement of the Transformer, it improves the target detection accuracy, which is conducive to improving the accuracy of UAV reconnaissance.
In recent years, the world's attention has made UAV surveillance a vital tool for combat reconnaissance. Target detection has taken the place of manual interpretation and has grown into a significant factor limiting UAV reconnaissance. Therefore, it is essential for battlefield reconnaissance to figure out how to increase the precision and speed of target detection. The fundamental issue addressed in this work is enhancing target detection accuracy while maintaining speed. YOLOv3 is a popular network structure in the industry because it is quick and precise compared to other network architectures. The attention mechanism, on the other hand, has a better detection impact on small and medium stimuli, whereas it just has a general detection effect on larger targets. The attention mechanism is implemented in YOLOv3 in this study.
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