Attentive Boundary-Aware Fusion for Defect Semantic Segmentation Using Transformer

Defect semantic segmentation is a pixel-level inspection technique to guarantee the quality of various products. It can obtain the precise location of defects by assigning a class label to each image pixel. Due to the confusing appearance of various defects, most existing defect semantic segmentation methods still suffer from the problem of intraclass difference and interclass indiscrimination. To tackle these challenges, we propose an attentive boundary-aware transformer framework, namely ABFormer, for segmenting different types of defects. Specifically, we propose a split-attention boundary-aware fusion (SABF) to split and integrate the boundary and context features with two different attention modules. It can enrich and fuse the feature maps more efficiently. Moreover, we propose a boundary-aware spatial attention module (BSAM) to capture the spatial interdependencies between the positions of the boundary features and the context features. This module can enhance the consistency of the defect features of the same class for solving the intraclass difference problem. Furthermore, we propose a boundary-aware channel attention module (BCAM) to model the semantic relationship between the channels of the boundary features and the context features. This module can reinforce the discrimination between the defect features of different classes for handling the interclass indiscrimination problem. Experimental results on three defect semantic segmentation datasets, namely NEU-Seg, MT-Defect, and MSD, demonstrate that our proposed method outperforms the state-of-the-art methods.