摘要
随着玻璃绝缘子在输电线路使用率越来越高,为适应当前无人机辅助巡检的模式,提出一种改进的YOLOv7的玻璃绝缘子自爆缺陷检测方法,满足输电线路巡检过程中对玻璃绝缘子缺陷检测需求。引入ST注意力机制,将其模块加入到YOLOv7网络模型第11层后面,对小目标以及不完整的绝缘子检测效果加强;改进坐标损失函数,定义SIOU损失函数公式,修正检测框位置。利用数据集CPLID中的部分玻璃绝缘子图像以及利用无人机拍摄得到的玻璃绝缘子图像对改进网络进行训练,结果表明,检测精度与速度都有明显提升。
With the increasing utilization rate of glass insulators in transmission lines,in order to adapt to the current mode of unmanned aerial vehicle(UAV)-assisted inspection,an modified YOLOv7 glass insulator self-explosion defect detection method is proposed to meet the demand for glass insulator defect detection in the process of transmission line inspection.ST attention mechanism was introduced,and its module was added to the 11th layer in YOLOv7 network model to strengthen the detection effect of small targets and incomplete insulators.The coordinate loss function was improved,the formula of SIOU loss function was redefined,and the position of detection box was corrected.Some glass insulator images in the data set CPLID and the glass insulator images taken by the UAV are used to train the modified network.The detection results show that the detection accuracy and speed are significantly improved.
作者
霍一凡
HUO Yi-fan(Datang Northeast Electric Power Test and Research Institute Co.,Ltd.)
出处
《电站系统工程》
2025年第3期39-42,共4页
Power System Engineering
