MRUNet: A two-stage segmentation model for small insect targets in complex environments

Abstract

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.