CS-SegFormer: a dual-path attention enhanced segmentation network for metallographic grain size evaluation

Grain size evaluation is a key factor in assessing the mechanical properties and service performance of stainless steel. However, conventional manual analysis of metallographic images is time-consuming, subjective, and often lacks reproducibility, limiting its industrial applicability. To overcome these challenges, this paper proposes CS-SegFormer (Channel and Spatial), an improved semantic segmentation framework tailored for metallographic image analysis of austenitic stainless steel. Built upon SegFormer, the proposed model integrates channel and spatial attention modules, which enhance the perception of grain boundaries, fine-scale textures, and complex microstructural variations.Experiments were conducted on a self-constructed dataset containing 120 ultra-high-resolution metallographic images, annotated into six categories including grains, twins, precipitates, and defects. Comparative results demonstrate that CS-SegFormer outperforms representative segmentation networks—UNet, KNet, SegFormer, and FastSCNN—achieving improvements of approximately 10% in both mIoU and mDice. These findings validate the superior accuracy and robustness of CSSegFormer and highlight its potential for reliable automated grain size grading in metallographic evaluation and industrial applications.