An Edge Recognition Method for Insulator State Based on Multi-dimension Feature Fusion - Dongmei HUANG; Yueqi WANG; Anduo HU; Jinzhong SUN; Shuai SHI; Yuan SUN; Lingfeng FANG (Zhongguo dianli)
Traditional insulator state recognition methods have such problems as poor real-time performance and insufficient feature extraction ability. Based on the idea of edge computing, this paper proposes a method for recognizing the insulator state based on multi-dimension feature fusion. An edge recognition framework for insulator state is constructed using cloud edge collaboration and edge federation collaboration. And a deep learning network integrating multi-dimension feature extraction is designed, which, by using the ResNet101 as the main feature extraction network, uses the Inception module to build the data pooling layer, and embeds the compression incentive module and convolution attention module to extract features from different dimensions. An insulator state recognition experiment is conducted using the data set of normal and defect states, and the average recognition accuracy reaches 99%. The experimental results have proved the validity of the proposed method.