首页|基于YOLO的输电线路鸟巢检测网络结构改进研究

基于YOLO的输电线路鸟巢检测网络结构改进研究

扫码查看
为提高电力系统的安全运行水平,针对输电线路上的鸟巢识别问题,提出基于YOLO的输电线路鸟巢检测网络.首先通过构建GhostNet模块搭建骨干网络,并优化了特征层提取方式;随后通过改进特征金字塔连接层,并结合PANet结构构建了瓶颈网络的特征金字塔,最终搭建了YOLO-NEST网络.构建并扩充数据集进行训练,将提出的网络与其他目标检测算法进行对比,结果表明所提网络在进行输电线路的鸟巢检测时效率更高.
Improvement of Bird's Nest Detection Network Structure of Transmission Lines Based on YOLO
In order to improve the safe operation level of power system,aiming at the problem of bird's nest identification on transmission lines,a bird's nest detection network based on YOLO is proposed.Firstly,the backbone network is built by constructing GhostNet module,and the feature layer extraction method is optimized.Then,by improving the feature pyramid connection layer and combining PANet structure,the feature pyramid of bottleneck network is constructed,and finally the YOLO-NEST network is built.A data set is constructed and expanded for training.The proposed network is compared with other target detection algorithms,it is concluded that the proposed network is more efficient in bird's nest detection of transmission lines.

bird's nesttarget detectionnetwork optimizationdetection platformYOLO

徐鹏雷、杨文刚

展开 >

广东电网有限责任公司惠州供电局,广东惠州 516000

华北电力大学,河北保定 071000

鸟巢 目标检测 网络优化 检测平台 YOLO

国家自然科学基金中央高校基本科研业务费专项

522012142022MS096

2024

智慧电力
陕西省电力公司

智慧电力

CSTPCD北大核心
影响因子:0.831
ISSN:1673-7598
年,卷(期):2024.52(4)
  • 27