Dual-Branch Discriminative Feature Cross-Attention Network for Lumber Surface Defect Segmentation

Lumber defect detection or segmentation is a crucial step in lumber industry. However, most existing semantic segmentation methods based on deep learning still remain challenging for lumber surface defect segmentation, because lumber defects often suffer from great variety, even if for the same defects. Moreover, lumber defects often have obscure boundaries and wide variations in size and shape. To address these issues, we develop a dual-branch discriminative feature cross-attention network (DBDFCNet) via U-Net for accurate defect segmentation. This network firstly introduces an improved Multi-scale Atrous Spatial Pyramid Pooling (MSASPP) to capture finer multi-scale features for enhancing the receptive field and addressing the wide range of defect sizes and shapes. Then, this network develops a dual-branch decoder which contains a binary branch and a semantic branch, in which the binary branch aims to determine defect localization and the semantic branch undertake the defect classification. In order to build the interaction between these two branches so that the binary branch to guide the semantic branch, we present a discriminative feature cross-attention module to increase inter-class distances and improving defect separability. Experimental results demonstrate that our method can effectively tackle the challenges of defect classification and edge segmentation for surface defect segmentation.