Zhendong He, Xiangyang Gao, Zhiyuan Liu, Xiaoyu An, Anping Zheng
School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, China.
School of Rail Transit Engineering, Zhengzhou Technical College, Zhengzhou, China.
Front Neurorobot. 2024 Apr 9;18:1397369. doi: 10.3389/fnbot.2024.1397369. eCollection 2024.
Rail surface defects present a significant safety concern in railway operations. However, the scarcity of data poses challenges for employing deep learning in defect detection. This study proposes an enhanced ACGAN augmentation method to address these issues. Residual blocks mitigate vanishing gradient problems, while a spectral norm regularization-constrained discriminator improves stability and image quality. Substituting the generator's deconvolution layer with upsampling and convolution operations enhances computational efficiency. A gradient penalty mechanism based on regret values addresses gradient abnormality concerns. Experimental validation demonstrates superior image clarity and classification accuracy compared to ACGAN, with a 17.6% reduction in FID value. MNIST dataset experiments verify the model's generalization ability. This approach offers practical value for real-world applications.
钢轨表面缺陷在铁路运营中是一个重大的安全问题。然而，数据的稀缺给在缺陷检测中应用深度学习带来了挑战。本研究提出一种增强的ACGAN增强方法来解决这些问题。残差块减轻了梯度消失问题，而谱归一化正则化约束判别器提高了稳定性和图像质量。用上采样和卷积操作替代生成器的反卷积层提高了计算效率。基于遗憾值的梯度惩罚机制解决了梯度异常问题。实验验证表明，与ACGAN相比，图像清晰度和分类准确率更高，FID值降低了17.6%。MNIST数据集实验验证了该模型的泛化能力。这种方法为实际应用提供了实用价值。
