Facing the challenges of regulating unmanned aerial vehicles (UAV),and based on an YOLOv5-Lite improved model,this paper incorporates an exponential moving sample weight function that dynamically allocates loss function weights to the model during the training iteration. Through model computations,we achieve real-time UAV tracking using a two-degree-of-freedom servo platform. Furthermore,video capture,model calculations,and servo control are all performed locally on a Raspberry Pi 4B.The optimized model maintains the original model's parameter count while achieving a mAP@.5:.95 score of 70.2％,representing a 1.5％ improvement over the baseline model. Real-time inference on the Raspberry Pi yields an average speed of 2.1 frames per second (FPS),demonstrating increased processing efficiency. Simultaneously,the Raspberry Pi controls a servo platform via the I2C protocol to track UAV targets,ensuring real-time dynamic monitoring of UAVs. This optimization enhances system reliability and offers superior practical value.
维普
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