Multimodal instance segmentation based on spatial axial band attention used in electrical distribution scenario

This paper presents a dual-modal instance segmentation framework based on Spatial Axial Band Attention (SABA), for electrical distribution scenarios. To address challenges of edge ambiguity, thermal heterogeneity, and small-target omission in single-modal methods, we propose: 1) A dual-stream feature pyramid with non-isotropic band partitioning, decomposing global attention into orthogonal local 2) A cross-scale guided aggregation mechanism to resolve intensity inhomogeneity caused by uneven heat dissipation. Experimental results on our established electrical distribution dataset demonstrate leading performance with 62.0 [email protected] and 56.3% mask IoU, surpassing single-modal baselines by 3.6-9.5% absolute gains. This lightweight architecture achieves lower complexity of 79.68 GFlops than some compared dual modal methods.