【摘要】 针对工业钢材表面缺陷检测过程中存在的样本采集困难、成本较高，以及缺陷种类较多难以覆盖全部导致的小样本问题，提出一种改进循环生成对抗网络(cycle-consistent generative adversarial networks, CycleGAN)的钢材表面缺陷图像生成方法。首先，将通道注意力(class activation map, CAM)和空间注意力(spatial attention map, SAM)机制嵌入到CycleGAN模型中，增强模型的特征提取能力；其次，引入权重解调(weight demodulation, WD)机制修复特征伪影和白斑，进一步提高生成图像的质量；再次，引入形状一致性损失对生成器训练过程进行监督，解决图像几何变换过程中内在模糊性问题；最后，将改进前后的模型在NEU-DET数据集上进行试验。结果表明，改进后的模型在缺陷图像生成的效果上更具多样性和准确性，PSNR,SSIM分别提高了13.0%和7.8%,FID值降低了33.1%。该方法能够稳定地生成高质量的各类钢材表面缺陷图像，可以达到增加训练数据的目的，对于其他缺陷数据集的扩增具有参考价值。更多还原

【Abstract】 Aiming at the difficulty of sample collection, high cost, and small sample problems caused by many types of defects and difficulty in covering all small samples in the process of industrial steel surface defect detection, a steel surface defect image generation method improved by cycle-consistent generation adversarial network(CycleGAN) was proposed. Firstly, channel attention map(CAM) and spatial attention map(SAM) were embedded in the CycleGAN model to enhance the feature extraction ability of the model. Secondly, the Weight Demodulation(WD) mechanism was introduced to fix feature artifacts and white spots, further improving the quality of the generated images. Thirdly, shape consistency loss was introduced to supervise the generator training process to solve the problem of inherent ambiguity in the process of image geometric transformation. Finally, the model before and after the improvement were experimented on the NEU-DET dataset. The results show that the improved model has more diversity and accuracy in the effect of defect image generation. PSNR and SSIM increase by 13.0% and 7.8% respectively, and FID values are reduced by 33.1%. This method can stably generate high-quality images of various steel surface defects, which can achieve the purpose of increasing training data, and also has reference value for the amplification of other defect datasets.更多还原