A two-stage substation equipment classification method based on dual-scale attention

Accurate classification of substation equipment images remains challenging due to various factors such as unexpected illumination, viewing angles, scale variations, shadows, surface contaminants, and different elements sharing similar appearances. This paper presents a novel two-stage substation equipment classification method based on dual-scale attention. Leveraging the region proposal technique from Faster-regions with CNN features (RCNN), the input images are initially decomposed into multiple scales to capture latent features. A dual-scale attention module is introduced to enhance the precision of feature extraction. Furthermore, a two-stage network is proposed to address the challenge of classifying closely similar substation equipment. A multi-layer perceptron performs a coarse classification to categorize the equipment into broad categories. Then, a lightweight classifier is employed for fine-grained subclassification, further distinguishing equipment within the same broad category. To mitigate the issue of limited training data, a specialized dataset is collected and annotated for the substation equipment classification. Experimental results demonstrate that the proposed method achieves remarkable accuracy, recall, and F1-score surpassing 0.91, outperforming mainstream approaches in terms of recall and F1 scores. Ablation experiments further validate the significant contributions of both the dual-scale attention and the two-stage classification module in improving the overall performance of the classification network.