Real-time semantic segmentation network for crops and weeds based on multi-branch structure

Weed recognition is an inevitable problem in smart agriculture, and to realise efficient weed recognition, complex background, insufficient feature information, varying target sizes and overlapping crops and weeds are the main problems to be solved. To address these problems, the authors propose a real-time semantic segmentation network based on a multi-branch structure for recognising crops and weeds. First, a new backbone network for capturing feature information between crops and weeds of different sizes is constructed. Second, the authors propose a weight refinement fusion (WRF) module to enhance the feature extraction ability of crops and weeds and reduce the interference caused by the complex background. Finally, a Semantic Guided Fusion is devised to enhance the interaction of information between crops and weeds and reduce the interference caused by overlapping goals. The experimental results demonstrate that the proposed network can balance speed and accuracy. Specifically, the 0.713 Mean IoU (MIoU), 0.802 MIoU, 0.746 MIoU and 0.906 MIoU can be achieved on the sugar beet (BoniRob) dataset, synthetic BoniRob dataset, CWFID dataset and self-labelled wheat dataset, respectively. First, the authors construct a new backbone network for capturing feature information between crops and weeds of different sizes. Second, a weight refinement fusion module is proposed to enhance the feature extraction ability of crops and weeds and reduce the interference caused by the complex background. At the same time, a semantic guided fusion is devised to enhance the interaction of information between crops and weeds and reduce the interference caused by overlapping goals. image