Method for Segmentation of Bean Crop and Weeds Based on Improved UperNet

Abstract

Bean crop and weed detection is a key component of precision agriculture, which can distinguish between bean crop and weeds. Accurate identification of weeds is essential for precision weed management. This study introduces PF-UperNet, a semantic segmentation approach rooted in an encoder-decoder architecture, designed to autonomously distinguish between bean crop and weeds using advanced computer vision techniques. Our methodology refines the foundational UperNet in several significant ways: Firstly, we adopt the PoolFormer-S12 as a substitute for UperNet’s backbone structure, aiming to reduce the model parameters and boost its performance metrics. Secondly, the Efficient Channel Attention (ECA) mechanism is integrated into both the PoolFormer-S12 and the Decoder, sharpening the network’s focus on extracting salient channel features. Then, within the Decoder, the Feature Alignment Pyramid Network (FaPN) supplants the conventional Feature Pyramid Network (FPN) module, remedying the misalignment issues observed in UperNet’s FPN feature maps. Lastly, we replace the Cross-Entropy loss with a combination of Cross-Entropy loss and Dice coefficient loss to increase the model’s attention on regions to be detected. Empirical evidence underlines the efficacy of our technique, with a Mean Intersection over Union (MIoU) of 87.45%, a Mean Pixel Accuracy (MPA) of 96.82%, and a total of 46.16M parameters encapsulated. Relative to the benchmark UperNet, our model demonstrates enhancements of 1.08% and 0.25% in MIoU and MPA, respectively, and accomplishes a parameter reduction of 27.92%. Experimental results demonstrate that the proposed model achieves remarkable detection performance in terms of MIoU, MPA, and model parameters. It can provide an effective detection method for weed management.

Keywords

• Improved UperNet
• green bean
• weeds
• semantic segmentation
• PoolFormer
• deep learning