Generalizing deep learning-based distress segmentation models for subway tunnel images by test-time training

Abstract

This paper proposes generalizing deep learning-based distress segmentation models for subway tunnel images by test-time training. Although it is promising to see that deep learning-based models are greatly alleviating the burden on subway tunnel maintenance workers, practical use of deep learning-based models for distress detection in subway tunnel images faces an obstacle, difficulty in training a generalizable model. Due to the diverse characteristics and different tunneling methods of the numerous subway tunnels, a model trained with data collected from one tunnel may not work well for another tunnel. Whereas training with data of a wide range of tunnels would be an ideal way, collecting such a large amount of well-labeled data is expensive. As an alternative to pursuing a highly generalizable model, it is more flexible and low-cost to generalize the model to specific test data at test time. In tasks of which the inference is not necessarily realtime, finetuning the model with the unlabeled test data may significantly improve the performance, not increasing too much inference time. In this paper, we focus on semantic segmentation of distress region in subway tunnel images and develop a test-time training method for generalizing the segmentation model to test data of different subway tunnels from the training data. Our method is simple yet effective, predicting pseudo labels with test-time batch normalization and finetuning the model with the pseudo labels. Extensive experimental results demonstrate that our method improves the distress segmentation performance in various scenarios, especially for crack which is a major and hard-to-detect distress type.