Shape-Consistent One-Shot Unsupervised Domain Adaptation for Rail Surface Defect Segmentation

Deep neural networks have greatly improved the performance of rail surface defect segmentation when the test samples have the same distribution as the training samples. However, in practical inspection scenarios, the rail surface exhibits variations in appearance due to different service times and natural conditions. Conventional deep learning models show limited generalization in scenes with distribution differences. To address this problem, we propose a novel one-shot unsupervised domain adaptation framework. Specifically, we introduce a shape-consistent style transfer module that performs pixel-level distribution alignment between the training and test images. Based on the one-shot test image, the training image is reconstructed to have the same appearance as the test image. Meanwhile, we employ a multitask learning strategy to prevent content distortion of the reconstructed images. To improve the robustness of the model to distribution differences, we design an edge-aware defect segmentation model and train the model using the reconstructed training images. The experimental results show that our method effectively improves the robustness of the model to distribution differences and achieves satisfying results in the task of rail surface defect segmentation.