全球能源互联网（英文）2022，Vol.5Issue(4)：397-408,12.DOI:10.1016/j.gloei.2022.08.006
基于深度自注意力网络和多因素相似度计算的变电站设备红外图像自动识别方法
Automatic infrared image recognition method for substation equipment based on a deep self-attention network and multi-factor similarity calculation
摘要
Abstract
Infrared image recognition plays an important role in the inspection of power equipment. Existing technologies dedicated to this purpose often require manually selected features, which are not transferable and interpretable, and have limited training data. To address these limitations, this paper proposes an automatic infrared image recognition framework, which includes an object recognition module based on a deep self-attention network and a temperature distribution identification module based on a multi-factor similarity calculation. First, the features of an input image are extracted and embedded using a multi-head attention encoding–decoding mechanism. Thereafter, the embedded features are used to predict the equipment component category and location. In the located area, preliminary segmentation is performed. Finally, similar areas are gradually merged, and the temperature distribution of the equipment is obtained to identify a fault. Our experiments indicate that the proposed method demonstrates significantly improved accuracy compared with other related methods and, hence, provides a good reference for the automation of power equipment inspection.关键词
变电站设备/红外图像智能识别/深度自注意力网络/多因素相似度计算Key words
Substation equipment/Infrared image intelligent recognition/Deep self-attention network/Multi-factor similarity calculation引用本文复制引用
李曜丞,许永鹏,胥明凯,王思源,谢志成,李喆,江秀臣..基于深度自注意力网络和多因素相似度计算的变电站设备红外图像自动识别方法[J].全球能源互联网（英文）,2022,5(4):397-408,12.基金项目
This work was supported by National Key R&D Program of China(2019YFE0102900). （2019YFE0102900）
