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.