In industrial production, defect detection is a critical task. Traditional methods often require manual visual inspection, which is low in accuracy and time-consuming. As a key technology in the development of deep learning, semantic segmentation can serve as an effective defect detection method, locating the defect position and providing pixel-level semantic segmentation results that describe the shape and size of the defect, significantly improving production efficiency and product quality.Based on the features of the DAGM 2007 industrial optical detection datasets, this paper uses the SegAN network architecture that combines GAN network with semantic segmentation model and makes adjustments and improvements to it. Because low-level semantic information, such as edges and textures, is important for industrial defect detection, more skip connections are introduced to the original architecture to improve the sensitivity of the network to low-level semantic information in the datasets.Experimental results demonstrate that our improvements can effectively improve the Dice and MIoU index of semantic segmentation, achieving significant performance improvement compared to the original architectures.