Symmetry-Guided Prototype Alignment and Entropy Consistency for Multi-Source Pedestrian ReID in Power Grids: A Domain Adaptation Framework

This study proposes a multi-source unsupervised domain adaptation framework for person re-identification (ReID), addressing cross-domain feature discrepancies and label scarcity in electric power field operations. Inspired by symmetry principles in feature space optimization, the framework integrates (1) a Reverse Attention-based Feature Fusion (RAFF) module aligning cross-domain features using symmetry-guided prototype interactions that enforce bidirectional style-invariant representations and (2) a Self-Correcting Pseudo-Label Loss (SCPL) dynamically adjusting confidence thresholds using entropy symmetry constraints to balance source-target domain knowledge transfer. Experiments demonstrate 92.1% rank-1 accuracy on power industry benchmarks, outperforming DDAG and MTL by 9.5%, with validation confirming robustness in operational deployments. The symmetric design principles significantly enhance model adaptability to inherent symmetry breaking caused by heterogeneous power grid environments.