基于FasterNet和YOLOv5改进的玻璃绝缘子自爆缺陷快速检测方法OA北大核心CSTPCD
Rapid Detection Method for Self-exploding Defects in Glass Insulators Based on Improved FasterNet and YOLOv5
为了实现对电力输电线路中绝缘子缺陷实时快速的巡检需求,提出了一种结合 FasterNet-tiny 和YOLOv5-s-v6.1 网络模型改进的缺陷快速检测算法 FasterNet-YOLOv5.首先引入参数量小推理速度更快的FasterNet网络替换原先的CSPDarkNet53主干网络,加快网络的检测速度.然后结合由GhostNetv2网络提出的解耦全连接注意力机制(decoupled fully connected,DFC),在主干特征提取…查看全部>>
In order to realize the real-time and fast inspection of insulator defects in power transmission lines,a fast defect detection arithmetic FasterNet-YOLOv5 is proposed by combining FasterNet-tiny and YOLOv5-s-v6.1 network model improvement.Firstly,a FasterNet network with a small number of parameters and faster reasoning speed is introduced to replace the original CSPDarkNet53 backbone network to speed up the detection speed of the network.Then,the DFC-Faster…查看全部>>
邬开俊;徐泽浩;单宏全
兰州交通大学电子与信息工程学院,兰州 730070兰州交通大学电子与信息工程学院,兰州 730070兰州交通大学电子与信息工程学院,兰州 730070
缺陷检测BiFPN-FFasterNetYOLOv5sDFC AttentionPConv
defect detectionBiFPN-FFasterNetYOLOv5sDFC AttentionPConv
《高电压技术》 2024 (5)
1865-1876,12
甘肃省自然科学基金(23JRRA913)内蒙古自治区重点研发与成果转化计划项目(2023YFSH0043).Project supported by Natural Science Foundation of Gansu Province(23JRRA913),Inner Mongolia Autonomous Region Key R&D and Achievement Trans-formation Program Project(2023YFSH0043).
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