Oil Leakage Detection Method for Power Grid Transformers Based on Improved Yolov4
It is particularly important for the safe and stable operation of power grid to find the oil leakage problem of power grid transformers in time.Traditional power transformer oil leakage detection mainly depends on manual regular detection,but manual de-tection cannot achieve all-weather monitoring and has lag.When the current mainstream object detection model is applied to the oil leakage detection of power grid transformers,there are some problems such as slow detection speed,low accuracy and poor robust-ness.It cannot meet the practical application.For this reason,an improved you only look once version 4(YoloV4)transformer oil leakage detection method is proposed.Firstly,by introducing Mobile Vision Transformer(Mobile-ViT)as the backbone structure of the model,the local and global information features of the object are effectively extracted by the convolution and Transformer struc-ture,which reduces the computation.Secondly,a multi-scale feature fusion layer is proposed,which aims to realize the multi-scale feature fusion of local and global information and enhance the context semantic expression,so as to better realize the oil leakage detec-tion of power grid transformers.The experimental results show that the detection accuracy of this method on the power grid trans-former oil leakage data set reaches 95.3％,and the detection speed reaches 50.6 fps;Compared with the native YoloV4 method,the detection accuracy is improved by 2.6％,and the detection speed by 2.6 fps.After practical application,the reasoning speed of this method deployed on edge devices also reaches 43 fps,which meets the needs of practical engineering.
power gridtransformerobject detectionoil leakage detectionYoloV4Mobile-ViTmulti-scale featuresfeature fusion
万方数据
维普
