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.
Real-time UAV monitoring is an important means of battlefield reconnaissance, and machine interpretation of UAV images has become the main form of image interpretation, so the merit of the algorithm becomes an important factor limiting UAV reconnaissance. To address the problems of insufficient graphics card arithmetic power, low detection accuracy and difficult deployment of algorithm models at the embedded end of UAVs, this paper proposes an improved lightweight target detection algorithm based on YOLOv5s, adding K-means++ algorithm and CA attention mechanism module to the original algorithm, and training the improved YOLOv5s-CA network using tank dataset, and the simulation results show that: the improved YOLOv5s-CA has an mAP value of 97.50%, an F1 value of 0.96, and an FPS value of 74.8, which can be deployed on UAVs for real-time detection.
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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