Data-Efficient Insulator Defect Detection in Power Transmission Systems via Multi-Granularity Feature Learning and Latent Context-Aware Fusion

Real-world power transmission inspection faces acute data scarcity and severe class imbalance, as defective insulator instances are exceptionally rare compared to normal samples. To enable robust defect detection under such constraints, we present MS-LaT—a backbone networkthat fuses multi-granularity feature learning with latent context-aware fusion. The architecture processes visual inputs through a streamlined pipeline: an input stage employing AdaptTeLU-augmented inverted multi-scale separable-residual convolutions to discern subtle local anomalies; a contextual reasoning stage powered by a Latent Transformer encoder with Multi-Head Latent Attention (MLA) for holistic scene understanding; and an output stage utilizing AdaptTeLU-refined inverted multi-scale convolutions to produce precise diagnostic decisions. Domain-adaptive batch normalization (AdaBN) is embedded to minimize cross-domain feature divergence, substantially boosting generalization across diverse operational environments. Research utilising real-world engineering datasets demonstrates the proposed method’s robust insulator defect detection capability in complex environments.