<p>Ablation study on top-k selection with 4 experts.</p>

At the high-level semantic stage of the backbone network, an Efficient Sparse Mixture-of-Experts module (ES-MoE) is incorporated. Through a conditional Top-k routing mechanism, ES-MoE enhances the diversity and discriminability of feature representations. Furthermore, we propose a Mixed Star Network (MSN), which selectively configures high-order feature modeling modules in a hierarchy-aware manner within a multi-branch structure, achieving a fine-grained trade-off between representational capacity and computational efficiency.

Building upon these components, a decoupled detection head is adopted to construct the unified EMamba detection framework. Extensive experiments on multiple steel surface defect datasets demonstrate that EMamba achieves competitive detection accuracy, structural defect perception capability, and inference efficiency compared with representative existing methods. These results indicate the effectiveness and practicality of the proposed hybrid modeling paradigm for steel surface defect detection.</p></div>