Glass Packaging Bottle Defect Segmentation via Differential Feature Selection and Spatial Feature Reconstruction

Transparent curved surfaces of glass packaging bottles produce structural responses that can obscure local appearance asymmetries associated with defects, complicating their segmentation across different scales and shapes. To address these challenges, we propose DFSR-Net, a glass bottle defect segmentation network. A Differential Context Modulation Module combines local differential responses with multiscale context to enhance weak anomalies while suppressing interference from normal bottle structures. A Key-Node Sparse Graph Propagation module uses key-node selection and restricted neighborhood aggregation to limit irrelevant cross-layer propagation and selectively transfer informative defect features. In the decoder, Dual-Receptive-Field Spatial Selection adaptively integrates local details and broader context, while Dual-Statistic Directional Calibration calibrates height- and width-wise spatial responses to improve region completeness and boundary representation. Experiments on the public Bottle Body dataset and the self-collected industrial glass bottle defect dataset show that DFSR-Net achieves mean Intersection over Union values of 81.08% and 80.14%. DFSR-Net also achieves a single-image processing speed of 74.3 FPS while maintaining high segmentation accuracy, suggesting potential for online glass bottle defect inspection.