Department of Mechanical and Mechatronics Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada.
Department of Automotive Engineering, Shinhan University, 95, Hoam-ro, Uijeongbu-si 11644, Republic of Korea.
Sensors (Basel). 2023 May 16;23(10):4793. doi: 10.3390/s23104793.
Railway defects can result in substantial economic and human losses. Among all defects, surface defects are the most common and prominent type, and various optical-based non-destructive testing (NDT) methods have been employed to detect them. In NDT, reliable and accurate interpretation of test data is vital for effective defect detection. Among the many sources of errors, human errors are the most unpredictable and frequent. Artificial intelligence (AI) has the potential to address this challenge; however, the lack of sufficient railway images with diverse types of defects is the major obstacle to training the AI models through supervised learning. To overcome this obstacle, this research proposes the RailGAN model, which enhances the basic CycleGAN model by introducing a pre-sampling stage for railway tracks. Two pre-sampling techniques are tested for the RailGAN model: image-filtration, and U-Net. By applying both techniques to 20 real-time railway images, it is demonstrated that U-Net produces more consistent results in image segmentation across all images and is less affected by the pixel intensity values of the railway track. Comparison of the RailGAN model with U-Net and the original CycleGAN model on real-time railway images reveals that the original CycleGAN model generates defects in the irrelevant background, while the RailGAN model produces synthetic defect patterns exclusively on the railway surface. The artificial images generated by the RailGAN model closely resemble real cracks on railway tracks and are suitable for training neural-network-based defect identification algorithms. The effectiveness of the RailGAN model can be evaluated by training a defect identification algorithm with the generated dataset and applying it to real defect images. The proposed RailGAN model has the potential to improve the accuracy of NDT for railway defects, which can ultimately lead to increased safety and reduced economic losses. The method is currently performed offline, but further study is planned to achieve real-time defect detection in the future.
铁路缺陷可能会导致巨大的经济和人员损失。在所有缺陷中，表面缺陷是最常见和最突出的类型，各种基于光学的无损检测（NDT）方法已被用于检测这些缺陷。在 NDT 中，可靠和准确地解释测试数据对于有效检测缺陷至关重要。在许多误差源中，人为误差是最不可预测和最频繁的。人工智能（AI）有潜力解决这一挑战；然而，缺乏具有各种类型缺陷的充足铁路图像是通过监督学习训练 AI 模型的主要障碍。为了克服这一障碍，本研究提出了 RailGAN 模型，该模型通过引入铁路轨道的预采样阶段来增强基础 CycleGAN 模型。为 RailGAN 模型测试了两种预采样技术：图像滤波和 U-Net。通过将这两种技术应用于 20 张实时铁路图像，结果表明 U-Net 在所有图像的图像分割中产生更一致的结果，并且受铁路轨道像素强度值的影响较小。在实时铁路图像上对 RailGAN 模型与 U-Net 和原始 CycleGAN 模型进行比较，结果表明原始 CycleGAN 模型会在不相关的背景中生成缺陷，而 RailGAN 模型只会在铁路表面生成合成的缺陷模式。RailGAN 模型生成的人工图像与铁路轨道上的真实裂缝非常相似，非常适合训练基于神经网络的缺陷识别算法。可以通过使用生成的数据集训练缺陷识别算法并将其应用于真实缺陷图像来评估 RailGAN 模型的有效性。所提出的 RailGAN 模型有可能提高铁路缺陷的 NDT 准确性，这最终可以提高安全性并减少经济损失。该方法目前是离线进行的，但计划进一步研究以实现未来的实时缺陷检测。
