摘要
针对无人机领域中的监管问题,基于YOLOv5-Lite的改进模型,提出了一种随着训练过程为模型动态地分配损失权重的指数移动样本加权函数。通过模型运算,控制二自由度云台对无人机实时跟踪,且视频采集、模型计算和二轴云台控制均在树莓派4B本地进行。优化过的模型在保持原模型参数量的同时,在mAP@.5:.95指标中达到了70.2%,相较于原模型提高了1.5%。在树莓派上的实时推理平均速度为2.1 FPS,处理效率更高。树莓派在模型推理的同时,通过I2C协议控制舵机平台对无人机目标进行追踪,保持对无人机的实时动态监测,提高了系统的可靠性,具有更好的实用价值。
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.
作者
陈浩安
李晖
黄瑞
符平博
张见
Chen Haoan;Li Hui;Huang Rui;Fu Pingbo;Zhang Jian(College of Electronics and Information Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,China;Graduate School of Chinese Aeronautical Establishment,Yangzhou 225006,China)
出处
《电子测量技术》
北大核心
2024年第6期182-189,共8页
Electronic Measurement Technology
基金
国家自然科学基金(61661018)项目资助。
