Dbrm-net: a dual-branch residual multimodal framework for intelligent visual inspection of insulator anomalies

Fine-grained insulator anomaly detection remains challenging because abnormal regions are often small, weakly contrasted, morphologically diverse, and embedded in repetitive structures or cluttered outdoor backgrounds. Conventional RGB-based detectors may fail to preserve subtle structural discontinuities, while direct feature fusion can introduce redundant or conflicting information. To address these issues, this study proposes DBRM-Net, a dual-branch residual framework that jointly processes RGB images and an RGB-derived Surface Structural Modality (SSM). Rather than introducing a new sensing modality or new low-level operators, SSM organizes complementary luminance, gradient, directional, Laplacian, roughness, and local-residual descriptors into a strictly aligned six-channel structural representation. This task-driven representation provides supplementary evidence for weak boundaries, local roughness variations, and subtle surface disturbances without requiring additional sensing hardware or cross-sensor registration. The Prior-Aware Mixture Fusion (PAMF) module models cross-branch agreement, discrepancy, and residual priors to improve forward feature integration, whereas the Adaptive Contextual Topology Injection Fusion (ACTIF) module selectively reuses anomaly-relevant structural evidence while preserving RGB semantic continuity. Experiments on two insulator anomaly datasets show that DBRM-Net achieves AP₅₀ and AP50:95 values of 88.7 and 65.4, respectively, on the primary IFD dataset. Compared with the strongest SSM-based baseline, the improvement is modest but consistent, while DBRM-Net uses fewer parameters, lower computational cost, and higher detector-side inference speed. Repeated-run, ablation, robustness, and visualization analyses further demonstrate stable optimization and complementary contributions from the structural representation and staged interaction modules. Overall, DBRM-Net provides a favorable accuracy-efficiency trade-off for fine-grained insulator anomaly detection under complex imaging conditions.