The internal defects of solar cells are the main reason for reducing the current conduction efficiency. The intensity of image defects after imaging by Electrolumine-scence （EL) or Photoluminescence （PL) varies greatly, and direct threshold segmentation will cause missed detection. This paper proposes a full-attention FSA-UNet network for hybrid defect segmentation in solar cells. Aiming at the characteristics of defect stratification, a feature enhancement module is designed to improve the ability to distinguish weak defects, and at the same time improve the backbone feature extraction network to speed up the detection efficiency of strong defects. This algorithm can accurately segment a variety of defects in single crystal silicon wafers. In order to verify the effectiveness of the algorithm in this paper, comparing the algorithm in this paper with U-net and DeepLabV3+, the best MIOU reaches 77.9%, which highlights the advantages of this algorithm.