The image segmentation of surface defects on solid oxide fuel cell (SOFC) is of great significance for the quality inspection of monolithic SOFC. Aiming at the problems of blurred edges and complex backgrounds of surface defect images of monolithic SOFC, this study proposed a self-attention fusion method for SOFC surface defect image segmentation. Firstly, a multi-channel self-attention module is proposed to enhance the inter-channel correlation and improve the channel representation. Secondly, a multi-scale attention fusion module is utilized to further improve the network’s ability to extract defect features at different scales; and finally, a triplet joint loss function is proposed to supervise the training process. Experiments show that the proposed method can effectively extract surface defects of monolithic SOFC while improving network segmentation performance.