  The current trend of automating inspections at substations has sparked a
surge in interest in the field of transformer image recognition. However, due
to restrictions in the number of parameters in existing models, high-resolution
images can't be directly applied, leaving significant room for enhancing
recognition accuracy. Addressing this challenge, the paper introduces a novel
improvement on deep self-attention networks tailored for this issue. The
proposed model comprises four key components: a foundational network, a region
proposal network, a module for extracting and segmenting target areas, and a
final prediction network. The innovative approach of this paper differentiates
itself by decoupling the processes of part localization and recognition,
initially using low-resolution images for localization followed by
high-resolution images for recognition. Moreover, the deep self-attention
network's prediction mechanism uniquely incorporates the semantic context of
images, resulting in substantially improved recognition performance.
Comparative experiments validate that this method outperforms the two other
prevalent target recognition models, offering a groundbreaking perspective for
automating electrical equipment inspections.
