Identification of Electrical Equipment Based on Faster LSTM-CNN Network
Document Type
Conference Proceeding
Publication Date
10-30-2020
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.
Identifier
85096352633 (Scopus)
ISBN
[9781728168531]
Publication Title
2020 IEEE International Conference on Networking Sensing and Control Icnsc 2020
External Full Text Location
https://doi.org/10.1109/ICNSC48988.2020.9238109
Recommended Citation
Xiong, Xiaoping; Xu, Shuang; Wu, Wenliang; Tu, Deran; Zhang, Jie; and Wei, Zhi, "Identification of Electrical Equipment Based on Faster LSTM-CNN Network" (2020). Faculty Publications. 4899.
https://digitalcommons.njit.edu/fac_pubs/4899
