针对智能制造中表面缺陷检测数据集不足问题，提出基于M-DCGAN的数据集增广方法。首先，向判别器添加上采样模块，搭建类U-Net结构并提升判别器与生成器的网络深度；设计基于Canny边缘检测的缺陷位置二值化掩膜提取方法;定义图像掩膜依赖的损失函数，建立缺陷目标位置关注引导机制；插入谱归一化层和Dropout层以提高训练稳定性,保持生成图像数据多样性。带钢缺陷数据集实验结果表明，该模型生成图片质量高于DCGAN、WGAN-GP和InfoGAN。采用本文M-DCGAN算法增广训练数据，能够显著提升并超过传统增广算法在 YOLOv5、SSD、Faster R-CNN、YOLOv3等八种经典方法中的缺陷检测精度，验证了本文算法的有效性。

Aiming at the insufficient surface defect dataset in intelligent manufacturing, an augmentation method based on mask deep convolution generative adversarial networks(M-DCGAN)  was proposed. Firstly, the U-Net-like structure was built by adding an upsampling module to the discriminator and enhancing network depth of the discriminator and generator,a binarization mask extraction method of defect position was designed based on Canny edge detection. Then, a defect target position attention guidance mechanism was established by defining the loss function  image mask dependent,the spectral normalization layer and dropout layer were inserted into the network to enhance training stability and maintain the generated images diversity. The experimental results on the strip steel defect dataset show that the images quality generated by the proposed model is higher than that of DCGAN, WGAN-GP, and InfoGAN. Enrich training data by the proposed M-DCGAN algorithm can significantly improve and surpass the defect detection accuracy of traditional augmentation algorithms in eight classic methods such as YOLOv5, SSD, Faster R-CNN, and YOLOv3, which verifies the effectiveness of the proposed algorithm.