Automatic concrete sleeper crack detection using a one-stage detector


Crack is the most common defect in the railway sleeper inspection work. However, it is still lack of effective algorithms to automatically detect. Two deep learning based methods were popularly used to detect cracks: two-stage methods and one-stage methods. However, they both have their corresponding shortcomings: for the two-stage methods, they are too slow; for the one-stage methods, their accuracy is a problem. In this paper, we propose using a divide-and-conquer strategy of labels to improve the accuracy of the one-stage methods. A one-stage crack detector called CF-NET is proposed by us including two main innovations: a new detection pipeline (CF module) and modified loss function smooth-flat. Finally, the proposed model CF-NET achieves 98.1% accuracy with 17 FPS real-time speed. The accuracy of CF-NET is matched with the two-stage method Faster R-CNN, but faster at least 3\(\times \). The meaning of our work is that we provide a real-time and high-accuracy crack detector to better meet the actual demands of the railway sleeper crack detection task.

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Funding was provided by China Scholarship Council (Grant no. 201906895026).

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Correspondence to Yunfang Peng.

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Xia, B., Cao, J., Zhang, X. et al. Automatic concrete sleeper crack detection using a one-stage detector. Int J Intell Robot Appl (2020).

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  • Automatic railway inspection
  • Concreate sleeper maintenance
  • Convolutional neural network (CNN)
  • Crack detection
  • Deep learning