Design of wind turbine oil level recognition system based on YOLOv5

Qitao Sun; Nana Lu; Lianda Duan; Suo Wang

doi:10.1117/12.2685639

15 August 2023 Design of wind turbine oil level recognition system based on YOLOv5

Qitao Sun, Nana Lu, Lianda Duan, Suo Wang

Proceedings Volume 12719, Second International Conference on Electronic Information Technology (EIT 2023); 127190E (2023) https://doi.org/10.1117/12.2685639
Event: Second International Conference on Electronic Information Technology (EIT 2023), 2023, Wuhan, China

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.

Citation Download Citation

Qitao Sun, Nana Lu, Lianda Duan, and Suo Wang "Design of wind turbine oil level recognition system based on YOLOv5", Proc. SPIE 12719, Second International Conference on Electronic Information Technology (EIT 2023), 127190E (15 August 2023); https://doi.org/10.1117/12.2685639

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