Abstract:To tackle the segmentation challenges arising from the complex morphology, significant size variations, blurred boundaries, and tight interconnections of superalloy grains, this paper presents a grain segmentation network integrated with a joint suppression attention mechanism that fuses channel and spatial information. The network combines the global modeling capacity of Swin Transformer and the local detail restoration capability of CNN, and embeds the aforementioned joint suppression attention mechanism, which integrates channel and spatial information, into the decoder. This mechanism effectively suppresses noise and texture interference, enhances the abilities of feature screening and generalization, and reinforces the fusion of shallow and deep features, thereby markedly improving the continuity of grain boundaries. Experimental results demonstrate that the proposed algorithm achieves 67.34% and 78.62% in terms of IoU and F1-score, respectively, on the self-constructed metallographic dataset, with all metrics outperforming those of mainstream grain segmentation algorithms for superalloys.