Design of wind turbine oil level recognition system based on YOLOv5

Abstract

In order to replenish the oil in the oil tank in time and reduce the loss caused by insufficient oil volume, we use wind turbines as an example to build a 1,300 oil tank picture dataset containing different oil levels. And the YOLOv5 network model based on the PyTorch framework trains the relevant dataset, and the oil tank and oil are detected through the training model to identify the oil volume of the oil tank, the model effect can meet industrial applications. The experimental results show that the YOLOv5 model established in this paper is 96% of the average accuracy of oil level recognition, which effectively solves the problem that the oil deficiency of oil tanks cannot be found in time.