A Study on Multimodal Data Fusion Based on Data Space for Power Equipment Identification

Since the image clarity of power equipment is easily disturbed by the external environment, the correct rate of recognition of power equipment by image recognition technology is easily affected. Based on this, this paper proposes a multimodal data fusion model for power equipment recognition. The Transformer model is selected to extract text features and image features separately, and then the features are fused and classified using a least squares support vector machine (LSSVM). Based on the data in the data space, the experimental part of this paper was completed. The experiments prove that the model in this paper has a greater advantage in the recognition of electrical equipment types, and the multimodal electrical equipment recognition results are better than the unimodal electrical equipment recognition results.