RI-Mamba: A Dual-branch Residual Interaction Network for Lightweight Insulator Segmentation

Abstract

In aerial images, slender and multi-scale insulator targets are often set against complex backgrounds, which imposes higher demands on the model's ability to retain details and capture long-range dependencies. To address this challenge, we propose a lightweight Dual-branch Residual Interaction Network (RI-Mamba) for insulator segmentation. Unlike typical single-branch architectures, RI-Mamba adopts a parallel dual-branch to prioritize the extraction of detail-semantic features. Specifically, the detail branch enhances the representation of local fine structures through feature expansion, while the semantic branch strengthens the correlation between positional and contextual semantics from a coordinate perspective. During the feature fusion stage, the proposed Residual Mamba Block (R-Mamba) discards traditional convolutional methods for processing local features and instead directly reuses detail-semantic information from the original features via a residual structure, achieving efficient feature reuse while avoiding parameter growth. Furthermore, a Gated Global-local Interaction Module (G2LIM) is introduced, which uses a learnable gated self-attention mechanism to dynamically adjust the fusion weights of global context and local details, thereby reducing redundant computation and enhancing feature adaptability. Experimental results on two public datasets demonstrate that RI-Mamba, with only 4.15M Params and 5.31G FLops, achieves mIoU of 86.28% and 91.59%, respectively, outperforming 17 lightweight insulator segmentation models. These results high ght that RI-Mamba achieves a favorable balance between computational efficiency and segmentation accuracy, making it well-suited for deployment in real-world power inspection scenarios. The code is available at: https://github.com/yaoshuang-yaobo/RI-Mamba.