Surface defect segmentation in metallic materials presents significant challenges due to irregular defect shapes, extreme scale variations ranging from small blowholes to large uneven regions, and low contrast with complex background textures. While Convolutional Neural Networks excel at local feature extraction, their limited receptive fields hinder effective global context modeling. Conversely, Vision Transformers capture long-range dependencies but struggle with fine-grained boundary details critical for accurate defect localization. To address these limitations, we propose FocusNet, a novel architecture integrating multi-scale feature refinement, hybrid attention mechanisms, and progress... More >