Model training parameters.

Rodent infestation is a critical factor contributing to grassland degradation, which significantly negatively affects grassland ecosystems. To assess rodent infestation on the plateau, there is an urgent need for a scientifically sound and effective method to detect the distribution of rodent burrows. In response, this study proposes a semantic segmentation approach utilizing the SegFormer model to detect rodent infestation in highland areas. First, we used an unmanned aerial vehicle to collect video data from the plateau and constructed a rodent burrows dataset after processing and precise labeling. Second, to address the issue of SegFormer’s suboptimal performance in segmenting small targets within complex backgrounds and among similar objects, we implemented targeted modifications to enhance its effectiveness for this task. Incorporating the efficient multi-scale attention (EMA) mechanism into SegFormer’s encoder improves the model’s capacity to capture global contextual information. Meanwhile, integrating the multi-kernel convolution feed-forward network (MCFN) into the decoder optimizes the problem of detail recovery and fusion of multi-scale features. We name this method EM-SegFormer (Efficient Multi-scale SegFormer). The experimental results demonstrate that the method achieves relatively good performance on the rodent burrows dataset. This study introduces a novel approach for plateau rodent infestation detection and offers reliable technical support for grassland restoration and management.