关键词: 图像检索, Swin Transformer, 交叉注意力机制, 中心损失, 层次聚类

Abstract: Existing image retrieval methods struggle to effectively distinguish and extract similar structural information and texture details of power equipment, resulting in lower retrieval accuracy. To solve this problem, a Power Image Retrieval method based on improved Swin Transformer (PIR-iSwinT) was proposed. Firstly, a Multi-Feature Structure Cross-Enhancement module (MFSCE) was introduced to enhance the model's perception of structural and edge features by combining the cross-attention mechanism of the gradient magnitude map. Secondly, an Adaptive Inter-class Difference Center Loss module (AIDCL) was designed to strengthen the model's ability to distinguish between similar and dissimilar samples. Finally, a Hierarchical Clustering Retrieval module (HCR) was constructed to optimize the sample matching strategy during retrieval and reduce computational complexity, further enhancing retrieval accuracy and efficiency. Experimental results on the self-built power scenario dataset and the NUS-WIDE dataset show that PIR-iSwinT achieves the highest mean retrieval precision of 96.76% and 92.68% at a 32-bit hash code length, outperforming HRMPA (Hash image Retrieval based on Mixed attention and Polarization Asymmetric loss) by 2.35% and 0.56%, respectively. Retrieval speed is also improved by a factor of three. These results indicate that PIR-iSwinT effectively extracts and distinguishes fine-grained structural features of power equipment, enhances retrieval efficiency, and demonstrates good generalization capability, verifying the effectiveness of the proposed method.

Key words: image retrieval, Swin Transformer, cross-attention mechanism, center loss, hierarchical clustering