UAV target detection algorithm based on improved YOLOv5s

Abstract

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.