Abstract
Recently, vision-based rail defect detection has attracted much attention owing to its practical significance. However, it still faces some challenges, such as high false alarm rate and poor feature robustness. With the development of deep neural networks (DNNs), deep learning based models have shown the potential to solve the problems. Nevertheless, these models usually require a large number of training samples, while collecting and labeling sufficient defective rail images is somewhat impractical. On the one hand, the probability of defect occurrence is low. On the other hand, we are not able to annotate samples that include all types of defects. To this end, we propose to generate defective training images in the digital space. In order to bridge the gap between virtual and real defective samples, this paper presents a domain adaptation based model for rail defect detection. The proposed method is evaluated on a real-world dataset. Experimental results show that our proposed method is superior to five established baselines.
This work was supported in part by the National Natural Science Foundation of China under Grant U2034211, in part by the Fundamental Research Funds for the Central Universities under Grant 2020JBZD010, in part by the Beijing Natural Science Foundation under Grant L191016 and in part by the China Railway R&D Program under Grant P2020T001.
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Notes
- 1.
[2021-05-30] https://github.com/adafruit/Adafruit_SSD1306.
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Cui, W. et al. (2021). From Digital Model to Reality Application: A Domain Adaptation Method for Rail Defect Detection. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13020. Springer, Cham. https://doi.org/10.1007/978-3-030-88007-1_10
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