Toward Efficient Power Scene Detection via Topology-Preserved Knowledge Distillation
Junfei Yi, Tengfei Liu, Jianxu Mao, Yaonan Wang, Hui Zhang, He Xie, Hang Zhong, Xiaojun Chang · IEEE Transactions on Industrial Informatics · 2025
The power industry relies on efficient inspection systems to ensure stability and safety. While deep learning has advanced automated inspection, its reliance on custom modules for specific tasks can impact efficiency. Knowledge distillation (KD) offers a balanced solution, but the complex textures and structures of power equipment challenge conventional KD methods, which often fail to capture essential local semantic and topological relationships. To address this, we proposeTopNet, a novel topology-preserved KD framework for power scene detection tasks. Specifically, we model the teacher’s knowledge as a graph, where nodes encode local fine-grained features and edges capture global topological relationships. Based on this, we introduce node feature distillation and edge feature distillation to transfer local–global structural knowledge, which can enhance the student’s ability to perceive objects. Furthermore, we also introduce aggregated feature distillation to incorporate and transfer contextual semantic knowledge. Comprehensive experiments are conducted on two different benchmark datasets to demonstrate that TopNet achieves state-of-the-art detection performance with high efficiency, offering a robust solution for automated power equipment inspection.