Identification of Electrical Equipment Based on Faster LSTM-CNN Network

Abstract

Power equipment inspection is one of the most important tasks to guarantee safe and stable operation of power grids. Although traditional power equipment detection methods are simple, their performances are not stable under complex outdoor environments. In this paper, we integrated the LSTM structure into the Faster R-CNN network, and designed a Faster LSTM-CNN network. We collected both normal samples and special samples, and used a variety of identification neural network models to conduct various experiments. The experimental results show that, compared with other methods such as Faster R-CNN and R-FCN, the proposed Faster LSTM-CNN network has better recognition performance for both normal samples and special samples.