When detecting objects in Unmanned Aerial Vehicle(UAV)taken images,large number of objects and high proportion of small objects bring huge challenges for detection algorithms based on the You Only Look Once(YOLO)frame...When detecting objects in Unmanned Aerial Vehicle(UAV)taken images,large number of objects and high proportion of small objects bring huge challenges for detection algorithms based on the You Only Look Once(YOLO)framework,rendering them challenging to deal with tasks that demand high precision.To address these problems,this paper proposes a high-precision object detection algorithm based on YOLOv10s.Firstly,a Multi-branch Enhancement Coordinate Attention(MECA)module is proposed to enhance feature extraction capability.Secondly,a Multilayer Feature Reconstruction(MFR)mechanism is designed to fully exploit multilayer features,which can enrich object information as well as remove redundant information.Finally,an MFR Path Aggregation Network(MFR-Neck)is constructed,which integrates multi-scale features to improve the network's ability to perceive objects of var-ying sizes.The experimental results demonstrate that the proposed algorithm increases the average detection accuracy by 14.15%on the Vis Drone dataset compared to YOLOv10s,effectively enhancing object detection precision in UAV-taken images.展开更多
面向第六代移动通信(Sixth generation of communication system,6G)网络全域立体覆盖与海量连接的需求,构建空天地一体化的高效传输体系已成为重要发展方向。然而,单一射频(Radio frequency,RF)或自由空间光(Free-space optical,FSO)...面向第六代移动通信(Sixth generation of communication system,6G)网络全域立体覆盖与海量连接的需求,构建空天地一体化的高效传输体系已成为重要发展方向。然而,单一射频(Radio frequency,RF)或自由空间光(Free-space optical,FSO)通信技术均存在固有局限,难以独立满足未来网络对超高速率、超高可靠与广域动态接入的综合要求。在此背景下,融合RF与FSO通信的互补优势构建智能协同的空天地一体化光电融合传输网络成为突破现有技术瓶颈的关键路径。本文系统综述了该领域的国内外研究进展,针对空天地一体化网络特征构建了基于光电融合的认知软件定义网络体系架构,重点阐述了适用于空天地异构环境的RF信道与FSO信道建模方法,深入剖析了高动态链路精准对准、异构资源智能分配、极端环境鲁棒传输等核心挑战。进而,围绕光电融合波束跟踪、自适应光电切换、光电并行协同传输及场景化链路选择等关键技术进行了详细论述。最后,展望了智能算法深度赋能、跨域抗扰动传输增强以及效能综合优化等未来发展趋势。研究表明,光电融合技术能够有效提升空天地一体化网络的综合性能,但其走向规模化应用仍需在跨层协同机制、动态资源管控及系统级效能评估等方面持续深化研究。展开更多
自由漂浮空间机器人(Free-floating space robots,FFSRs)凭借其运动自由度高、工作寿命长等优势,已成为长期在轨服务的关键无人设备。然而空间环境变化导致的外部扰动,以及因燃料消耗、系统参数辨识不准确等因素导致的模型不确定性会增...自由漂浮空间机器人(Free-floating space robots,FFSRs)凭借其运动自由度高、工作寿命长等优势,已成为长期在轨服务的关键无人设备。然而空间环境变化导致的外部扰动,以及因燃料消耗、系统参数辨识不准确等因素导致的模型不确定性会增加空间机器人高精度控制的难度。本文针对自由漂浮空间机器人存在外部扰动和模型不确定性的场景,设计了一种基于扰动补偿的模型预测控制方法。基于固定时间稳定性理论设计扰动观测器,使扰动估计误差在不依赖于初始误差的常数上界内实现收敛。同时,将扰动估计值补偿入模型预测控制器,提高集总扰动条件下预测模型的准确性,进一步地利用模型预测控制滚动优化的特点,实现了空间机器人约束条件下高精度控制。本文证明了扰动观测器与基于扰动补偿模型预测控制器的稳定性,并通过数值仿真验证了方法的有效性。展开更多
基金co-supported by the National Natural Science Foundation of China(No.62103190)the Natural Science Foundation of Jiangsu Province,China(No.BK20230923)。
文摘When detecting objects in Unmanned Aerial Vehicle(UAV)taken images,large number of objects and high proportion of small objects bring huge challenges for detection algorithms based on the You Only Look Once(YOLO)framework,rendering them challenging to deal with tasks that demand high precision.To address these problems,this paper proposes a high-precision object detection algorithm based on YOLOv10s.Firstly,a Multi-branch Enhancement Coordinate Attention(MECA)module is proposed to enhance feature extraction capability.Secondly,a Multilayer Feature Reconstruction(MFR)mechanism is designed to fully exploit multilayer features,which can enrich object information as well as remove redundant information.Finally,an MFR Path Aggregation Network(MFR-Neck)is constructed,which integrates multi-scale features to improve the network's ability to perceive objects of var-ying sizes.The experimental results demonstrate that the proposed algorithm increases the average detection accuracy by 14.15%on the Vis Drone dataset compared to YOLOv10s,effectively enhancing object detection precision in UAV-taken images.
文摘面向第六代移动通信(Sixth generation of communication system,6G)网络全域立体覆盖与海量连接的需求,构建空天地一体化的高效传输体系已成为重要发展方向。然而,单一射频(Radio frequency,RF)或自由空间光(Free-space optical,FSO)通信技术均存在固有局限,难以独立满足未来网络对超高速率、超高可靠与广域动态接入的综合要求。在此背景下,融合RF与FSO通信的互补优势构建智能协同的空天地一体化光电融合传输网络成为突破现有技术瓶颈的关键路径。本文系统综述了该领域的国内外研究进展,针对空天地一体化网络特征构建了基于光电融合的认知软件定义网络体系架构,重点阐述了适用于空天地异构环境的RF信道与FSO信道建模方法,深入剖析了高动态链路精准对准、异构资源智能分配、极端环境鲁棒传输等核心挑战。进而,围绕光电融合波束跟踪、自适应光电切换、光电并行协同传输及场景化链路选择等关键技术进行了详细论述。最后,展望了智能算法深度赋能、跨域抗扰动传输增强以及效能综合优化等未来发展趋势。研究表明,光电融合技术能够有效提升空天地一体化网络的综合性能,但其走向规模化应用仍需在跨层协同机制、动态资源管控及系统级效能评估等方面持续深化研究。
文摘自由漂浮空间机器人(Free-floating space robots,FFSRs)凭借其运动自由度高、工作寿命长等优势,已成为长期在轨服务的关键无人设备。然而空间环境变化导致的外部扰动,以及因燃料消耗、系统参数辨识不准确等因素导致的模型不确定性会增加空间机器人高精度控制的难度。本文针对自由漂浮空间机器人存在外部扰动和模型不确定性的场景,设计了一种基于扰动补偿的模型预测控制方法。基于固定时间稳定性理论设计扰动观测器,使扰动估计误差在不依赖于初始误差的常数上界内实现收敛。同时,将扰动估计值补偿入模型预测控制器,提高集总扰动条件下预测模型的准确性,进一步地利用模型预测控制滚动优化的特点,实现了空间机器人约束条件下高精度控制。本文证明了扰动观测器与基于扰动补偿模型预测控制器的稳定性,并通过数值仿真验证了方法的有效性。
