针对当前反无人机作战研究热点,以要地反无人机装备体系需求分析为输入,基于美国国防部体系结构框架(department of defense architecture framework,DoDAF)标准,采用基于模型的体系架构(model-based systems engineering,MBSE)设计方法...针对当前反无人机作战研究热点,以要地反无人机装备体系需求分析为输入,基于美国国防部体系结构框架(department of defense architecture framework,DoDAF)标准,采用基于模型的体系架构(model-based systems engineering,MBSE)设计方法,构建反无人机装备体系,完成体系结构建模和方法设计,选取典型作战视角和系统视角描述模型,给出较为全面、直观的反无人机装备体系顶层概念框架,可为未来反无人机作战及装备发展提供思路与借鉴。展开更多
The YOLOx-s network does not sufficiently meet the accuracy demand of equipment detection in the autonomous inspection of distribution lines by Unmanned Aerial Vehicle(UAV)due to the complex background of distribution...The YOLOx-s network does not sufficiently meet the accuracy demand of equipment detection in the autonomous inspection of distribution lines by Unmanned Aerial Vehicle(UAV)due to the complex background of distribution lines,variable morphology of equipment,and large differences in equipment sizes.Therefore,aiming at the difficult detection of power equipment in UAV inspection images,we propose a multi-equipment detection method for inspection of distribution lines based on the YOLOx-s.Based on the YOLOx-s network,we make the following improvements:1)The Receptive Field Block(RFB)module is added after the shallow feature layer of the backbone network to expand the receptive field of the network.2)The Coordinate Attention(CA)module is added to obtain the spatial direction information of the targets and improve the accuracy of target localization.3)After the first fusion of features in the Path Aggregation Network(PANet),the Adaptively Spatial Feature Fusion(ASFF)module is added to achieve efficient re-fusion of multi-scale deep and shallow feature maps by assigning adaptive weight parameters to features at different scales.4)The loss function Binary Cross Entropy(BCE)Loss in YOLOx-s is replaced by Focal Loss to alleviate the difficulty of network convergence caused by the imbalance between positive and negative samples of small-sized targets.The experiments take a private dataset consisting of four types of power equipment:Transformers,Isolators,Drop Fuses,and Lightning Arrestors.On average,the mean Average Precision(mAP)of the proposed method can reach 93.64%,an increase of 3.27%.The experimental results show that the proposed method can better identify multiple types of power equipment of different scales at the same time,which helps to improve the intelligence of UAV autonomous inspection in distribution lines.展开更多
文摘针对当前反无人机作战研究热点,以要地反无人机装备体系需求分析为输入,基于美国国防部体系结构框架(department of defense architecture framework,DoDAF)标准,采用基于模型的体系架构(model-based systems engineering,MBSE)设计方法,构建反无人机装备体系,完成体系结构建模和方法设计,选取典型作战视角和系统视角描述模型,给出较为全面、直观的反无人机装备体系顶层概念框架,可为未来反无人机作战及装备发展提供思路与借鉴。
基金supported by the National Natural Science Foundation of China under Grants 62362040,61662033supported by the Science and Technology Project of the State Grid Jiangxi Electric Power Co.,Ltd.of China under Grant 521820210006.
文摘The YOLOx-s network does not sufficiently meet the accuracy demand of equipment detection in the autonomous inspection of distribution lines by Unmanned Aerial Vehicle(UAV)due to the complex background of distribution lines,variable morphology of equipment,and large differences in equipment sizes.Therefore,aiming at the difficult detection of power equipment in UAV inspection images,we propose a multi-equipment detection method for inspection of distribution lines based on the YOLOx-s.Based on the YOLOx-s network,we make the following improvements:1)The Receptive Field Block(RFB)module is added after the shallow feature layer of the backbone network to expand the receptive field of the network.2)The Coordinate Attention(CA)module is added to obtain the spatial direction information of the targets and improve the accuracy of target localization.3)After the first fusion of features in the Path Aggregation Network(PANet),the Adaptively Spatial Feature Fusion(ASFF)module is added to achieve efficient re-fusion of multi-scale deep and shallow feature maps by assigning adaptive weight parameters to features at different scales.4)The loss function Binary Cross Entropy(BCE)Loss in YOLOx-s is replaced by Focal Loss to alleviate the difficulty of network convergence caused by the imbalance between positive and negative samples of small-sized targets.The experiments take a private dataset consisting of four types of power equipment:Transformers,Isolators,Drop Fuses,and Lightning Arrestors.On average,the mean Average Precision(mAP)of the proposed method can reach 93.64%,an increase of 3.27%.The experimental results show that the proposed method can better identify multiple types of power equipment of different scales at the same time,which helps to improve the intelligence of UAV autonomous inspection in distribution lines.
