Dual-channel convolutional neural network for power edge image recognition

Journal of Cloud Computing

Yi Ma¹  Fangrong Zhou¹  Gang Lin²  Bo Wang³ 

[1] Electric Power Research Institute, Yunnan Power Grid Company ltd, Kunming, China;Nanjing power supply branch of state grid Jiangsu electric power co., LTD, Nanjing, China;School of Electrical Engineering and Automation, Wuhan University, Wuhan, China;

关键词: Dual-channel;    Convolution neural network;    Power equipment;    Random forests;    Image recognition;   

DOI  :  10.1186/s13677-021-00235-9

来源: Springer

【 摘 要 】

In view of the low accuracy and poor processing capacity of traditional power equipment image recognition methods, this paper proposes a power equipment image recognition method based on a dual-channel convolutional neural network (DC-CNN) model and random forest (RF) classification. In the aspect of feature extraction, the DC-CNN model extracts the characteristics of power equipment through two independent CNN models. In the aspect of the recognition algorithm, by referring to the advantages of the traditional machine learning method and incorporating the advantages of the RF, an RF classification method incorporating deep learning is proposed. Finally, the proposed DC-CNN model and RF classification method are used to classify images of various types of power equipment. The results show that the proposed methods can be effectively applied to the image recognition of various types of power equipment, and they greatly improve the recognition rate of power equipment images.

【 授权许可】

CC BY