Abstract
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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