Multi angle location and identification method of suspension insulators based on R2CNN algorithm

With the continuous development of smart grids, power inspections have become intelligent and sophisticated. This paper proposes a method based on inclined boxes for the automatic position recognition and diagnosis of suspension insulators under a visible light channel. The rotational region convolutional neural networks (R2CNN) algorithm is used to extract the features of large sample images of suspension insulators, and the model is trained to identify and select insulated devices in any direction. The open-source TensorFlow software is used as the identification tool and is combined with related tuning strategies to optimize the model during the training process. The final model’s recognition accuracy was 89.73%. The results prove that this method overcomes the limitations of using axis-aligned boxes for detection, which can provide more accurate position information for diagnoses of suspension insulators. The model has strong robustness in the changing environment, and has certain innovation value and engineering significance.