Semantic Segmentation Algorithm of Animal Husbandry Image Based on an Improved U⁃Net Network

Abstract

Due to the limitations of unclear edges and fuzzy features in image segmentation tasks, this study proposes an enhanced U⁃Net semantic segmentation network utilizing the local and global fusion attention module in response to the drawbacks of fuzzy features and unclear edges in image segmentation tasks. Firstly, a feature extraction module combining convolution and Transformer is introduced in the bottleneck layer, so that the network can fully simultaneously capture local and global features, and effectively promote the fusion of local and global features. Secondly, the CBAM attention module is added to the skip connections between the encoder and decoder. Finally, the output feature map is processed using the ASPP module to enhance focus on target features and improve segmentation performance. Experiments conducted on four animal husbandry segmentation datasets show that the LCA_Net model proposed in this study achieves an IoU score of 90.19% and a Dice score of 94.83%, compared with U-Net and other mainstream segmentation networks, it has improved. This study offers effective technical support for advancing aquaculture status monitoring and lays a foundation for further development in this field.