摘要
由无人机代替人工进行电力绝缘子巡检具有重要意义,针对无人机的上位机算力和存储资源有限的问题,提出一种适用于绝缘子掉串故障检测的实时目标检测改进算法.以YOLOv5s检测网络为基础,将颈部结构中路径聚合网络替换为双向特征金字塔网络,以提升特征融合能力;使用DIoU优化损失函数,对模型进行γ系数的通道剪枝和微调,总体上提升检测网络的精度、速度和部署能力;在网络输出处进行图像增强以提升算法可用性.在特殊扩增的绝缘子故障数据集下测试,相较于原始的YOLOv5s算法,改进算法在精度平均值上提升了3.91%,速度提升了25.6%,模型体积下降了59.1%.
It is of great significance for unmanned aerial vehicle(UAV) to replace manual inspection of power insulators. Aimed at the problem of limited computing power and storage resources of the UAV, an improved real-time target detection algorithm suitable for insulator drop string failure detection is proposed. Based on the YOLOv5 s detection network, first, the PANet networks in neck are replaced with bi-directional feature pyramid network(BiFPN) to improve the feature fusion ability. Next, DIoU is used to optimize the loss function to optimize the model. The channel pruning and fine tuning of the γ coefficient generally improve the accuracy, speed, and deployment ability of the detection network. Finally, the image is enhanced at the network output to improve the availability of the algorithm. The proposed algorithm is tested under a specially expanded insulator fault data set. The results show that compared with the original YOLOv5 s algorithm, the average accuracy of the proposed algorithm is improved by 3.91%, the detection speed is improved by 25.6%, and the model volume is reduced by 59.1%.
作者
李登攀
任晓明
颜楠楠
LI Dengpan;REN Xiaoming;YAN Nannan(School of Electrical Engineering,Shanghai Dianji University,Shanghai 201306,China;State Grid East China Electric Power Test and Research Institute Co.,Ltd.,Shanghai 200437,China;Institute of Science and Technology for Brain-Inspired Intelligence,Hehai University,Shanghai 200433,China)
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2022年第8期994-1003,共10页
Journal of Shanghai Jiaotong University
基金
上海市科技重大专项(2018SHZDZX01)。
关键词
无人机
绝缘子掉串
双向特征金字塔网络结构
γ系数剪枝微调
DIoU损失函数
图像增强
unmanned aerial vehicle(UAV)
insulator drop string
bi-directional feature pyramid network(BiFPN)
γcoefficient pruning fine adjustment
DIoU loss function
image enhancement
