Semantic Segmentation-Based Identification and Quantitative Analysis of Cross-Sectional Quality Features in Luzhou-Flavor Liquor Daqu

The objective evaluation of Daqu cross-sectional quality is challenging due to its heterogeneous structure, small features, and low contrast. This study proposes a semantic-segmentation-based framework for the automated identification and quantitative analysis of Luzhou-flavor Daqu cross-sections. Four representative architectures—including three convolutional neural network (CNN)-based models (U-Net, U-Net++, and U2-Net) and one Transformer-based model (SegFormer)—were systematically benchmarked. To address severe class imbalance and enhance model robustness, a task-specific data augmentation pipeline was implemented. With these optimized augmentation strategies, the U2-Net model demonstrated the best performance, with a peak mean Intersection over Union (mIoU) of 87.54% and a Dice score of 98.30%. Based on the predicted masks, quantitative indicators such as plaque area ratio, pizhang thickness, and fissure length were precisely extracted. The proposed framework provides an objective and scalable solution for Daqu quality inspection, offering significant practical value for industrial scenarios involving complex materials and fine-grained defect patterns.