田间害虫在线自动识别是农田害虫防治的重要辅助手段。在实际应用中，由于目标小、物种相似性高、背景复杂等因素，昆虫在线识别系统往往无法对目标进行准确定位和识别。为方便昆虫幼虫的识别，本研究提出了一种两阶段分割方法MRUNet。在结构上，MRUNet借鉴了Mask R-CNN在进行语义分割之前先进行目标检测的做法，并且使用改进的轻量级UNet进行语义分割。为了可靠地评价所提出模型的分割结果，除了常用的语义分割评价指标外，本研究还引入了统计方法来衡量模型在样本间性能的稳定性。实验结果表明，所提出的两阶段图像分割策略能有效处理复杂背景下的小目标。与现有的语义分割方法相比，MRUNet在相同条件下具有更好的稳定性和细节处理能力。本研究可以为昆虫幼虫识别提供高质量的图像数据。

Online automated identification of farmland pests is an important auxiliary means of pest control.  In practical applications, the online insect identification system is often unable to locate and identify the target pest accurately due to factors such as small target size, high similarity between species and complex backgrounds.  To facilitate the identification of insect larvae, a two-stage segmentation method, MRUNet was proposed in this study.  Structurally, MRUNet borrows  the practice of object detection before semantic segmentation from Mask R-CNN and then uses an improved lightweight UNet to perform the semantic segmentation.  To reliably evaluate the segmentation results of the models, statistical methods were introduced to measure the stability of the performance of the models among samples in addition to the evaluation indicators commonly used for semantic segmentation.  The experimental results showed that this two-stage image segmentation strategy is effective in dealing with small targets in complex backgrounds.  Compared with existing state-of-the-art semantic segmentation methods, MRUNet shows better stability and detail processing ability under the same conditions.  This study provides a reliable reference for the automated identification of insect larvae.