To address the challenges in industrial surface defect detection, such as variable morphology, low contrast and the frequent missing of small targets. A novel framework for semantic segmentation is presented, which leverages an edge-guided encoder-decoder architecture and local-global Mamba (GLMamba) to realize the synergy of "edge enhancement, global-local encoding and dual-path decoding".Firstly, a local-global Mamba encoder is built. Local details and global context are collaboratively modeled through the deployment of local and global Mamba blocks at shallow and deep layers, respectively.Secondly, an edge feature extraction module (EGFM) is designed, where edge feature maps are explicitly generated by fusing Sobel gradients with multi-scale features and are subsequently enhanced by an edge attention module (EAM). To further inject edge information into the backbone network, a multi-scale boundary gating (MSBG) mechanism is introduced to dynamically modulate encoder and decoder features. The decoder employs a dual-path attention module, where spatial details and channel context are fused in parallel to optimize multi-scale feature fusion efficiency and semantic consistency. Experimental results on the NEU-Seg, MT-Defect and FSSD-12 datasets demonstrate the effectiveness and strong generalization capability of the proposed model.