Research on Recognition Technology of Transformer Oil Leakage Based on Improved YOLOv3
In the defect detection of transformer oil leakage, deep learning technology has been widely used. However, the application cases of image recognition for transformer oil leakage rely heavily on expensive computing resources, and lightweight computing networks have gradually received more and more attention. This paper designs a partial residual network structure based on the gradient concept combination theory, realizes the lightweight design of the model, and applies it to the application of transformer oil leakage defect detection, to a certain extent, enhances the deep convolution network learning ability. Experimental results show that, while maintaining the same accuracy as YOLOv3 model, the YOLOv3 P model can reduce the amount of parameters by 55% and reduce the amount of calculation by 35% and doubles the calculation speed compared with the YOLOv3 model. It has great practical value in engineering application of transformer oil leakage.
2020 International Conference on Computer Information and Big Data Applications (CIBDA)