U-MoEMamba: A Hybrid Expert Segmentation Model for Cabbage Heads in Complex UAV Low-Altitude Remote Sensing Scenarios

To address the challenges of missed and incorrect segmentation in cabbage head detection under complex field conditions using UAV-based low-altitude remote sensing, this study proposes U-MoEMamba, an innovative dynamic state-space framework with a mixture-of-experts (MoE) collaborative segmentation network. The network constructs a dynamic multi-scale expert architecture, integrating three expert paradigms—multi-scale convolution, attention mechanisms, and Mamba pathways—for efficient and accurate segmentation. First, we design the MambaMoEFusion module, a collaborative expert fusion block that employs a lightweight gating network to dynamically integrate outputs from different experts, enabling adaptive selection and optimal feature aggregation. Second, we propose an MSCrossDualAttention module as an attention expert branch, leveraging a dual-path interactive attention mechanism to jointly extract shallow details and deep semantic information, effectively capturing the contextual features of cabbages. Third, the VSSBlock is incorporated as an expert pathway to model long-range dependencies via visual state-space representation. Evaluation on datasets of different cabbage growth stages shows that U-MoEMamba achieves an mIoU of 89.51% on the early-heading dataset, outperforming SegMamba and EfficientPyramidMamba by 3.91% and 1.4%, respectively. On the compact heading dataset, it reaches 91.88%, with improvements of 2.41% and 1.65%. This study provides a novel paradigm for intelligent monitoring of open-field crops.