A visual Mamba-based global-local framework for instance segmentation of highly overlapping multi-scale bubbles in dense two-phase flows

To address the identification and segmentation challenges caused by large bubble size variations, dense distributions, and severe overlap and occlusion in gas–liquid two-phase flows, a global-local fusion segmentation model named global-local bubble network is designed. The model uses the visual state space encoder as the backbone for feature extraction. By utilizing the two-dimensional selective scan mechanism with linear complexity, it establishes long-range pixel dependencies, significantly enhancing context inference capabilities under dense occlusion. Simultaneously, a global-local slicing interface is proposed, which effectively eliminates edge truncation artifacts inherent in high-resolution image tiling through overlap-based slicing augmentation during training and boundary-aware reconstruction inference during inference, ensuring the topological integrity of cross-scale targets. Finally, an adaptive receptive field aggregation neck incorporating a selective Kernel Atrous spatial pyramid pooling module is introduced to dynamically adjust receptive field weights and fuse deep semantic features with shallow texture features, thereby effectively improving the segmentation accuracy of multi-scale bubbles. Compared with mainstream single-stage and multi-stage instance segmentation frameworks, the proposed model shows superior segmentation performance and robustness in simulation experiments. Its comprehensive F1-score reaches 99.89%, and the Mean Intersection over Union (mIoU) reaches 95.11%. Applied to real bubbly flow experiments, it attains an average F1-score of 91.55% and an average mIoU of 94.39% under rigorous fivefold cross-validation, maintaining superior and stable performance under complex conditions. This provides a reliable solution for the refined parameter statistics of complex multiphase flow fields.