WAVELET-GUIDED BRANCH-CONDITIONED GROUPED NORMALIZING FLOW FOR POWER-LINE ANOMALY DETECTION

UAV power-line inspection requires anomaly detectors to distinguish equipment faults from substantial changes in background, viewpoint, and illumination. We propose Wavelet-Guided Branch-Conditioned Grouped Normalizing Flow(WBGFlow), a shared conditional density model that organizes complementary observations for this setting. RAW appearance, low-frequency structure, and high frequency energy retain distinct feature groups throughout the flow. Per-block identity embeddings adapt shared coupling subnetworks to each observation source. All three branches learn normality jointly, while the final score selects RAW and LL latent energies. This connects complementary-observation training with explicit control over anomaly-ranking evidence. On InsPLAD-fault, WBGFlow achieves the highest mean image-level AUROC among the compared methods at 94.62%, averaged over two training seeds, exceeding DifferNet by 2.16 percentage points in macro-average with gains in all five evaluated categories. A complementary MVTecAD evaluation yields 95.78%. Component ablations examine the roles of grouping and identity conditioning, while fixed-model scoring analysis supports separating training observations from ranking contributions. Together, the results demonstrate the value of identity-preserving shared density modeling for uncontrolled power-line inspection.