To solve the problem of scarcity of bolt defect samples in transmission lines, we propose a bolt defect image generation method based on dual discriminator architecture and pseudo-enhancement strategy (DP-GAN). First, we propose a residual discriminator network structure, coupled with a dual discriminator GAN architecture, to enhance the diversity of generation while preserving image feature information. Then, a generated image fidelity assessment method is designed to evaluate the fidelity of generated images by fitting the real dataset and screening out high-quality fake samples. Finally, a new pseudo-enhanced training strategy is proposed, which uses pseudo-samples to augment the few-shot dataset, which solves the problem of poor generation quality due to too few images of bolt defects. We construct a few-shot bolt defect dataset and conduct experiments on this dataset. Experimental results demonstrate that the bolt defect images generated by our proposed method have better quality and richer diversity than other image generation methods. Additionally, the proposed method significantly improves the performance of bolt defect classification. The classification accuracy shows a significant improvement over the CNN-only baseline.

中文翻译：

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DP-GAN：基于双鉴别器架构和伪增强策略的输电线路螺栓缺陷生成网络


为了解决输电线路中螺栓缺陷样本稀缺的问题，我们提出了一种基于双判别器架构和伪增强策略的螺栓缺陷图像生成方法（DP-GAN）。首先，我们提出了一种残差判别器网络结构，结合双判别器 GAN 架构，在保留图像特征信息的同时增强生成的多样性。然后，设计了一种生成图像保真度评估方法，通过拟合真实数据集并筛选出高质量的假样本来评估生成图像的保真度。最后，提出了一种新的伪增强训练策略，使用伪样本来增强少样本数据集，解决了由于螺栓缺陷图像太少而导致生成质量差的问题。我们构建了一些螺栓缺陷数据集并在此数据集上进行了实验。实验结果表明，与其他图像生成方法相比，我们提出的方法生成的螺栓缺陷图像具有更好的质量和更丰富的多样性。此外，所提出的方法显着提高了螺栓缺陷分类的性能。分类精度比仅使用 CNN 的基线有了显着提高。